As a result, companies have been looking for ways to speed up the process. One of the solutions that has recently emerged is low-code and no-code (LCNC) development tools. These tools allow users to create applications without writing much if any code.

Low-code development has become increasingly popular in recent years, and Gartner predicts that it will account for over two-thirds of application development by 2024. A study by Statista has also found that low/no-code development tool spending will grow from just under $13 billion in 2020 to $65 billion by 2027.

In the same way tools like Canva and Visme have empowered a new generation of graphic designers, no-code and low-code platforms are giving rise to a new breed of citizen developers. These people with little or no coding experience are using these tools to build working applications.

There are many reasons why software engineering is moving in this direction. Below, we discuss a few reasons the LCNC approach is the key to faster engineering.

Also read: Democratizing Software Development with Low-Code

What are Low-Code and No-Code Tools?

First, it’s important to distinguish between no-code and low-code platforms. No-code platforms allow users to create working applications without writing any code. They are becoming popular with business users because they enable them to solve problems independently and optimize day-to-day processes without waiting for IT to do it.

On the other hand, low-code platforms require some coding but aim to make the process easier with drag-and-drop interfaces and prebuilt components. They are aimed at professional developers and allow them to build applications faster by automating some of the more tedious tasks involved in coding, such as creating boilerplate code or scaffolding.

How Low/No-Code Accelerates Software Engineering

There are several reasons why LCNC is the key to faster engineering.

Ease of use

One of the biggest advantages of LCNC development platforms is they are much easier to use than traditional coding environments. This is because they provide a graphical user interface (GUI) that allows users to drag and drop components to build applications. No-code platforms take this a step further by not requiring any coding.

This ease of use means users don’t need to be skilled programmers to build an application. It opens up the possibility for anyone to create working applications without any coding experience.

Speed

LCNC development is a powerful tool for software engineering teams. It speeds up the development process by allowing developers to create sophisticated applications visually. Users can accelerate requirements gathering, prototype faster, and save time on wireframes and complex coding. In addition, LCNC development tools often come with prebuilt libraries of code, which can further speed up the development process.

Agile iteration

The ability to quickly experiment and test new ideas is essential to maintaining a competitive edge. Open-source, low-code development platforms enable web developers to prototype and deploy new applications with minimal effort rapidly.

There is no need for lengthy development cycles or complex code; developers can add new features quickly and easily. This makes it possible to experiment with new ideas and get feedback from users rapidly, making it easier to improve upon them.

Easy data integration

Developers can quickly and easily build applications that connect to, work with, and consolidate data from various sources. This means they can spend less time worrying about the technical details of data integration and more time focusing on building great applications.

Lower costs and easier scalability

Another advantage of using a LCNC development platform is that it can save money. No-code platforms, in particular, have the potential to reduce development costs by allowing businesses to build applications without having to hire expensive developers.

In addition, LCNC platforms are often much easier to scale than traditional coding environments. This is because they are designed to be modular, so users can add new features quickly and easily.

Mobile experiences optimization

LCNC development platforms make it easy to optimize applications for mobile devices. For example, they allow developers to create responsive designs that automatically adapt to any screen size.

Thus, users can quickly and easily create applications that look great on any device without worrying about coding for specific devices.

Better application life cycle management

LCNC development platforms often come with built-in tools for managing the life cycle of applications. This includes features such as version control to keep track of changes to code and collaboration tools to work with other developers on the team.

This makes it easier to manage the development process and ensure applications are always up-to-date.

SaaS integration without programming

Low-code development is often associated with app creation, but it can be useful for much more. Low-code platforms offer an easy way to connect data and operations, making them ideal for integrating with software-as-a-service (SaaS) applications. This is especially important for businesses that rely on customer relationship management (CRM) or marketing solutions. With a low-code platform, users can quickly and easily connect applications to the tools needed without spending hours coding custom integrations.

Also read: Effectively Using Low-Code/No-Code in the Developer Cycle

Limitations of Low-Code Platforms

We would be remiss if we failed to mention some of the limitations of low-code platforms.

Limited capability for complexity

One limitation is that low-code platforms are inadequate for complex applications. This is because they often lack the flexibility of traditional coding environments. They are typically suitable for customer-facing applications, web and mobile front ends, and business process or workflow applications but are not ideal for infrastructure deployment, back-end APIs (application programming interfaces), and intensive customization.

Many tools are not enterprise-grade

Another limitation is that low-code platforms are not always suitable for enterprise-grade applications. This is because they often lack the security and scalability features required for large-scale applications.

Getting Started With Low-Code and No-Code Development

Despite these limitations, all indications are that these tools will keep getting better and better. And as they do, they will become more and more popular. So, if you’re looking to start low-code development, now is the time.

There are a few things you should keep in mind when getting started, such as:

• What type of application do you want to build?
• What is your budget?
• How much time do you have to build your application?
• What is your level of coding experience?

If you can answer these questions, you’ll be well on your way to finding the right LCNC platform for your needs. However, if you’re unsure where to start, check out this low-code cheat sheet.

Read next: 10 User-Centered Software Design Mistakes to Avoid

The post Why Low-Code/No-Code is the Key to Faster Engineering appeared first on IT Business Edge.

In this introductory tutorial, you’ll learn the basics of Python for machine learning, including different model types and the steps to take to ensure you obtain quality data, using a sample machine learning problem. In addition, you’ll get to know some of the most popular libraries and tools for machine learning.

Jump to:

• Machine Learning 101
• Python Libraries and Tools
• Training a Machine Learning Algorithm with Python Using the Iris Flowers Dataset

Also read: Best Machine Learning Software

Machine Learning 101

Machine learning (ML) is a form of artificial intelligence (AI) that teaches computers to make predictions and recommendations and solve problems based on data. Its problem-solving capabilities make it a useful tool in industries such as financial services, healthcare, marketing and sales, and education among others.

Types of machine learning

There are three main types of machine learning: supervised, unsupervised, and reinforcement.

Supervised learning

In supervised learning, the computer is given a set of training data that includes both the input data (what we want to predict) and the output data (the prediction). The computer then learns a model that maps input to output data to make predictions on new, unseen data.

Unsupervised learning

In unsupervised learning, the computer is only given the input data. The computer then learns to find patterns and relationships in the data and applies this to things like clustering or dimensionality reduction.

You can use many different algorithms for machine learning. Some popular examples include:

• Linear regression
• Logistic regression
• Decision trees
• Random forests
• Support vector machines
• Naive bayes
• Neural networks

The choice of algorithm will depend on the problem you are trying to solve and the available data.

Reinforcement learning

Reinforcement learning is a process where the computer learns by trial and error. The computer is given a set of rules (the environment) and must learn how to maximize its reward (the goal). This can be used for things like playing games or controlling robots.

The steps of a machine learning project

Data import

The first step in any machine learning project is to import the data. This data can come from various sources, including files on your computer, databases, or web APIs. The format of the data will also vary depending on the source.

For example, you may have a CSV file containing tabular data or an image file containing raw pixel data. No matter the source or format, you must load the data into memory before doing anything with it. This can be accomplished using a library like NumPy, Scikit Learn, or Pandas.

Once the data is loaded, you will usually want to scrutinize it to ensure everything looks as expected. This step is critical, especially when working with cluttered or unstructured data.

Data cleanup

Once you have imported the data, the next step is to clean it up. This can involve various tasks, such as removing invalid, missing, or duplicated data; converting data into the correct format; and normalizing data. This step is crucial because it can make a big difference in the performance of your machine learning model.

For example, if you are working with tabular data, you will want to ensure all of the columns are in the proper format (e.g., numeric values instead of strings). You will also want to check missing values and decide how to handle them (e.g., imputing the mean or median value).

If you are working with images, you may need to resize or crop them to be the same size. You may also want to convert images from RGB to grayscale.

Also read: Top Data Quality Tools & Software

Splitting data into training/test sets

After cleaning the data, you’ll need to split it into training and test sets. The training set is used to train the machine learning model, while the test set evaluates the model. Keeping the two sets separate is vital because you don’t want to train the model on the test data. This would give the model an unfair advantage and likely lead to overfitting.

A standard split for large datasets is 80/20, where 80% of the data is used for training and 20% for testing.

Model creation

Using the prepared data, you’ll then create the machine learning model. There are a variety of algorithms you can use for this task, but determining which to use depends on the goal you wish to achieve and the existing data.

For example, if you are working with a small dataset, you may want to use a simple algorithm like linear regression. If you are working with a large dataset, you may want to use a more complex algorithm like a neural network.

In addition, decision trees may be ideal for problems where you need to make a series of decisions. And random forests are suitable for problems where you need to make predictions based on data that is not linearly separable.

Model training

Once you have chosen an algorithm and created the model, you need to train it on the training data. You can do this by passing the training data through the model and adjusting the parameters until the model learns to make accurate predictions on the training data.

For example, if you train a model to identify images of cats, you will need to show it many photos of cats labeled as such, so it can learn to recognize them.

Training a machine learning model can be pretty complex and is often an iterative process. You may also need to try different algorithms, parameter values, or ways of preprocessing the data.

Evaluation and improvement

After you train the model, you’ll need to evaluate it on the test data. This step will give you a good indication of how well the model will perform on unseen data.

If the model does not perform well on the test data, you will need to go back and make changes to the model or the data. This is often the usual scenario when you first train a model—you must go back and iterate several times until you get a model that performs well.

This process is known as model tuning and is an integral part of the machine learning workflow.

Also read: Top 7 Trends in Software Product Design for 2022

Python Libraries and Tools

There are several libraries and tools that you can use to build machine learning models in Python.

Scikit-learn

One of the most popular libraries is scikit-learn. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN.

The library is built on NumPy, SciPy, and Matplotlib libraries. In addition, it includes many utility functions for data preprocessing, feature selection, model evaluation, and input/output.

Scikit-learn is one of the most popular machine learning libraries available today, and you can use it for various tasks. For example, you can use it to build predictive models for classification or regression problems. You can also use it for unsupervised learning tasks such as clustering or dimensionality reduction.

NumPy

NumPy is another popular Python library that supports large, multi-dimensional arrays and matrices. It also includes several routines for linear algebra, Fourier transform, and random number generation.

NumPy is widely used in scientific computing and has become a standard tool for machine learning problems.

Its popularity is due to its ease of use and efficiency; NumPy code is often much shorter and faster than equivalent code written in other languages. In addition, NumPy integrates well with other Python libraries, making it easy to use in a complete machine learning stack.

Pandas

Pandas is a powerful Python library for data analysis and manipulation. It’s commonly used in machine learning applications for preprocessing data, as it offers a wide range of features for cleaning, transforming, and manipulating data. In addition, Pandas integrates well with other scientific Python libraries, such as NumPy and SciPy, making it a popular choice for data scientists and engineers.

At its core, Pandas is designed to make working with tabular data easier. It includes convenient functions for reading in data from various file formats; performing basic operations on data frames, such as selection, filtering, and aggregation; and visualizing data using built-in plotting functions. Pandas also offers more advanced features for dealing with complex datasets, such as join/merge operations and time series manipulation.

Pandas is a valuable tool for any data scientist or engineer who needs to work with tabular data. It’s easy to use and efficient, and it integrates well with other Python libraries.

Matplotlib

Matplotlib is a Python library that enables users to create two-dimensional graphics. The library is widely used in machine learning due to its ability to create visualizations of data. This is valuable for machine learning problems because it allows users to see patterns in the data that they may not be able to discern by looking at raw numbers.

Additionally, you can use Matplotlib to create simulations of machine learning algorithms. This feature can be helpful for debugging purposes or for understanding how the algorithm works.

Seaborn

Seaborn is a Python library for creating statistical graphics. It’s built on top of Matplotlib and integrates well with Pandas data structures.

Seaborn is often used for exploratory data analysis, as it allows you to create visualizations of your data easily. In addition, you can use Seaborn to create more sophisticated visualizations, such as heatmaps and time series plots.

Overall, Seaborn is a valuable tool for any data scientist or engineer who needs to create statistical graphics.

Jupyter Notebook

The Jupyter Notebook is a web-based interactive programming environment that allows users to write and execute code in various languages, including Python.

The Notebook has gained popularity in the machine learning community due to its ability to streamline the development process by allowing users to write and execute code in the same environment and inspect the data frequently.

Another reason for its popularity is its graphical user interface (GUI), which makes it easier to use than command-line editors such as Terminal and VS Code. For example, it isn’t easy to visualize and inspect data that contains several columns in a command-line editor.

Training a Machine Learning Algorithm with Python Using the Iris Flowers Dataset

For this example, we will be using the Jupyter Notebook to train a machine learning algorithm with the classic Iris Flowers dataset.

Although the Iris Flowers dataset is small, it will allow us to demonstrate how to use Python for machine learning. This dataset has been used extensively in pattern recognition and machine learning literature. It is also relatively easy to understand, making it a good choice for our first problem.

The Iris Flowers dataset contains 150 observations of Iris flowers. The goal is to take measurements of flowers and use that data to predict what species of Iris it is based on the following physical parameters of three Iris species:

• Versicolor
• Setosa
• Virginica

Installing Jupyter Notebook with Anaconda

Before getting started with training the machine learning algorithm, we will need to install Jupyter. To do so, we will use a platform known as Anaconda.

Anaconda is a free and open-source distribution of the Python programming language that includes the Jupyter Notebook. It also has various other useful libraries for data analysis, scientific computing, and machine learning. 

Jupyter Notebook with Anaconda is a powerful tool for any data scientist or engineer working with Python, whether using Windows, Mac, or Linux operating systems (OSs).

Visit the Anaconda website and download the installer for your operating system. Follow the instructions to install it, and launch the Anaconda Navigator application.

To do this on most OSs, you must open a terminal window, type jupyter notebook, and hit Enter. This action will start the Jupyter Notebook server on your machine.

It also automatically displays the Jupyter Dashboard in a new browser window pointing to your Localhost at port 8888.

Creating a new notebook

Once you have Jupyter installed, you can begin training your machine learning algorithm. Start by creating a new notebook.

To create a new notebook, select the folder where you want to store the new notebook and then click the New button in the upper right corner of the interface and select Python [default]. This action will create a new notebook with Python code cells.

New notebooks are automatically opened in a new browser tab named Untitled. You can rename it by clicking Untitled. For our tutorial, rename it Iris Flower.

Importing a dataset into Jupyter

We’ll get our dataset from the Kaggle website. Head over to Kaggle.com and create a free account using a custom email, Google, or Facebook.

Next, find the Iris dataset by clicking Datasets in the left navigation pane and entering Iris Flowers in the search bar.

The CSV file contains 150 records under five attributes—petal length, petal width, sepal length, sepal width, and class (species)—so there are only five columns in total.

Once you’ve found the dataset, click the Download button, and ensure the download location is the same as that of your Jupyter Notebook. Unzip the file to your computer.

Next, open Jupyter Notebook and click on the Upload button in the top navigation bar. Find the dataset on your computer and click Open. You will now upload the dataset to your Jupyter Notebook environment.

Data preparation

We can now import the dataset into our program. We’ll use the Pandas library for this. This pre-prepared dataset doesn’t have much to do with data preparation.

Start by typing the following code into a new cell and click run:

import pandas as pd

iris=pd.read_csv(‘Iris.csv’)

iris

This first line will import the Pandas library into our program, allow us to use it, and rename it pd.

The second line will read the CSV file and store it in a variable called iris. View the dataset by typing iris and running the cell.

You should see something similar to the image below:

As you can see, each row represents one Iris flower with its attributes listed in the columns.

The first four columns are the attributes or features of the Iris flower, and the last column is the class label which corresponds to a species of Iris Flower, such as Iris setosa, Iris virginica, etc.

Before proceeding, we need to remove the ID column because it can cause problems with our classification model. To do so, enter the following code in a new cell.

iris.drop(columns = ‘Id’, inplace = True)

Type iris once more to see the output. You will notice the Id column has been dropped.

Understanding the Data

Now that we know how to import the dataset let’s look at some basic operations we can perform to understand the data better.

First, let’s see what data types are in our dataset. To do this, we’ll use the dtypes attribute of the dataframe object. Type the following code into a new cell and run it:

iris.dtypes

You should see something like this:

You can see that all of the columns are floats except for the Species column, which is an object. This is because objects in Pandas are usually strings.

Now let’s examine some summary statistics for our data using the describe function. Type the following code into a new cell and run it:

iris.describe

You can see that this gives us some summary statistics for each column in our dataset.

We can also use the head and tail functions to look at the first and last few rows of our dataset, respectively. Type the following code into a new cell and run it:

iris.head()

Then type:

iris.tail()

We can see the first five rows of our dataframe correspond to the Iris setosa class, and the last five rows correspond to the Iris virginica.

Next, we can visualize the data using several methods. For this, we will need to import two libraries, Matplotlib and Seaborn.

Type the following code into a new cell:

import seaborn as sns

import matplotlib.pyplot as plt

You will also need to set the style and color codes of Seaborn. Additionally, the current Seaborn version generates warnings that we can ignore for this tutorial. Enter the following code:

sns.set(style=”white”, color_codes=True)

import warnings

warnings.filterwarnings(“ignore”)

For the first visualization, create a scatter plot using Matplotlib. Enter the following code in a new cell.

iris.plot(kind=”scatter”, x=”SepalLengthCm”, y=”SepalWidthCm”)

This will generate the following output:

However, to color the scatterplot by species, we will use Seaborn’s FacetGrid class. Enter the following code in a new cell.

sns.FacetGrid(iris, hue=”Species”, size=5) \

  .map(plt.scatter, “SepalLengthCm”, “SepalWidthCm”) \

  .add_legend()

Your output should be as follows:

As you can see, Seaborn has automatically colored our scatterplot, so we can visualize our dataset better and see differences in sepal width and length for the three different Isis species.

We can also create a boxplot using Seaborn to visualize the petal length of each species. Enter the following code in a new cell:

sns.boxplot(x=”Species”, y=”PetalLengthCm”, data=iris)

You can also extend this plot by adding a layer of individual points using Seaborn’s striplot. Type the following code in a new cell:

ax = sns.boxplot(x=”Species”, y=”PetalLengthCm”, data=iris)

ax = sns.stripplot(x=”Species”, y=”PetalLengthCm”, data=iris, jitter=True, edgecolor=”gray”)

Another possible visualization is the kernel density plots (KD Plots) which shows the probability density. Enter the following code:

sns.FacetGrid(iris, hue=”Species”, size=6) \

  .map(sns.kdeplot, “PetalLengthCm”) \

  .add_legend()

A Pairplot is another useful Seaborn visualization. It shows the relationships between all columns in our dataset. Enter the following code into a new cell:

sns.pairplot (iris, hue=”Species”, size=3)

The output should be as follows:

From the above, you can quickly tell the Iris setosa species is separated from the rest across all feature combinations.

Similarly, you can also create a Boxplot grid using the code:

iris.boxplot(by=”Species”, figsize=(12, 6))

Let’s perform one final visualization that places each feature on a 2D plane. Enter the code:

from pandas.plotting import radviz

radviz(iris, “Species”)

Split the data into a test and training set

Having understood the data, you can now proceed and begin training the model. But first we need to split our data into a training and test set. To do this, we will use a function known as train_test_split from the scikit-learn library. This action will divide our data set into a ratio of 70:30 (Our dataset is small hence a higher test set).

Enter the following code in a new cell:

from sklearn.metrics import confusion_matrix

from sklearn.metrics import classification_report

from sklearn.model_selection import train_test_split

Next, separate the data into dependent and independent variables:

X = iris.iloc[:, :-1].values

y = iris.iloc[:, -1].values

Split into a training and test set:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)

The confusion matrix we imported is a table that is often used to evaluate the performance of a machine learning algorithm. The matrix comprises four quadrants, each representing the predicted and actual values for one of the two classes.

The first quadrant represents the true positives, or the observations correctly predicted to be positive. The second quadrant represents the false positives, which are the observations that were incorrectly predicted to be positive. The third quadrant represents the false negatives, which are the observations that were incorrectly predicted to be negative. Finally, the fourth quadrant represents the true negatives, or the observations correctly predicted to be negative.

The matrix rows represent the actual values, while the columns represent the predicted values.

Train the model and check accuracy

We will train the model and check the accuracy using four different algorithms: logistic regression, random forest classifier, decision tree classifier, and multinomial naive bayes.

To do so, we will create a series of objects in various classes and store them in variables. Be sure to take note of the accuracy scores.

Logistic regression

Enter the code below in a new cell:

from sklearn.linear_model import LogisticRegression

classifier = LogisticRegression()

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

from sklearn.metrics import accuracy_score

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Random forest classifier

Enter the code below in a new cell:

from sklearn.ensemble import RandomForestClassifier

classifier=RandomForestClassifier(n_estimators=100)

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Decision tree classifier

Enter the code below in a new cell:

from sklearn.tree import DecisionTreeClassifier

classifier = DecisionTreeClassifier()

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Multinomial naive bayes

Enter the following code in a new cell:

from sklearn.naive_bayes import MultinomialNB

classifier = MultinomialNB()

classifier.fit(X_train, y_train)

y_pred = classifier.predict(X_test)

print(classification_report(y_test, y_pred))

print(confusion_matrix(y_test, y_pred))

print(‘accuracy is’,accuracy_score(y_pred,y_test))

Evaluating the model

Based on the training, we can see that three of our four algorithms have a high accuracy of 0.97. We can therefore choose any of these to evaluate our model. For this tutorial, we have selected the decision tree, which has high accuracy.

We will give our model sample values for sepal length, sepal width, petal length, and petal width and ask it to predict which species it is.

Our sample flower has the following dimensions in centimeters (cms):

• Sepal length: 6
• Sepal width: 3
• Petal length: 4
• Petal width: 2

Using a decision tree, enter the following code:

predictions = classifier.predict([[6,3,4,2]])

classifier.predict([[6,3,4,2]])

The output result is Iris-virginica.

Some Final Notes

As an introductory tutorial, we used the Iris Flowers dataset, which is a straightforward dataset containing only 150 records. Our training set only has 45 records (30%), hence similar accuracies with most of the algorithms.

However, in a real-world situation, the dataset may have thousands or millions of records. That said, Python is well-suited for handling large datasets and can easily scale up to higher dimensions.

Read next: Kubernetes: A Developers Best Practices Guide

The post Python for Machine Learning: A Tutorial appeared first on IT Business Edge.

What Is Performance Testing?

Performance testing helps dev teams and system and network engineers evaluate how well an application works and what it can do. It can determine how much a program can handle before it crashes and identifies any instability in the program.

Often, developers will have a set of requirements they need to meet, and performance testing can help determine whether or not the dev team has been successful. For example, an e-commerce application may need to be able to handle hundreds or thousands of users at once. Performance testing tools could simulate a large number of browsers accessing the application at once and see how it performs, looking at load speed, stability, and resource utilization to see if any changes are needed. The technology is sometimes called application performance testing (APM).

Also read: How to Choose a Software Development Methodology: 6 Approaches

Key Features of Software Performance Testing Tools

IT and dev teams looking for software performance testing tools should look for at least the following features.

Real or Emulated Browsers

If IT teams need to know how many users an application can handle before it crashes, they need a performance testing tool that offers real or emulated browsers. This allows developers and IT teams to simulate a number of users accessing the application at once to see how it holds up. Additionally, they can see how the software looks and acts on different browsers (Chrome, Firefox, Safari, etc.) without having to actually download all of the browsers on their device.

Without this feature, an engineer would have to use a variety of devices and browsers to run the same tests, which could significantly delay the entire project.

Automated Testing

Automated testing features help reduce the workload on human system engineers, which are already hard to come by. The software engineer growth rate is only about 8 percent, meaning there will soon be more open roles than there are engineers to fill them. And the gap is only growing as tech workers become overworked and burn out. With automated testing, devs and sysadmins can avoid tedious testing tasks, only jumping in to fix issues that the system finds or to run specialized tests.

Need help keeping your software engineers? Learn more about Motivating and Retaining Your Development Team.

Artificial Intelligence

Artificial intelligence (AI) can also lessen the burden on dev and systems engineers by identifying dependencies in an application and prioritizing issues. This prevents engineers from adjusting one part of an application without addressing functions of the application that are dependent on that piece. AI can also predict system failures before they happen, allowing sysadmins to avoid problems and address the issues sooner.

Learn more about How AI is Shaping Software Development.

Best Performance Testing Tools

The following list contains some of the best performance testing tools, chosen for their high user reviews and helpful features.

Radview WebLOAD

Radview WebLOAD provides performance and load testing with the ability to simulate a large number of virtual users at once. It’s available on the cloud in a fully-managed version or on-premises for self-hosting. The dashboards and reports are flexible and customizable, allowing developers to get the insights they need. WebLOAD has three subscription tiers, but the actual pricing information is not available on the website.

Key Features

• Intelligent test integrated development environment (IDE)
• Virtual user simulation
• Customizable dashboards and reports
• On-premises or cloud-based solutions
• Support for a variety of web protocols

Pros

• Easy to use and customize
• Helpful and responsive support team
• Efficient scripting language

Cons

• Documentation is limited
• Licenses are tied to specific devices

LoadNinja

LoadNinja provides an easy-to-use interface for performance testing, complete with instant playback and real browsers to help ensure accuracy. The system makes it easy to create web and API load tests, and it provides real-time feedback on performance issues. Users can also automate user interface and API testing, so they can focus on more complex issues. There are four pricing tiers available starting at $99/month, with greater savings provided for purchasing a full year at once.

Key Features

• Real browsers for load testing
• Script playback and recording
• Automated load testing
• Unlimited load tests
• Private proxies for internal application testing

Pros

• Accurate and detailed reporting
• Easy to create complex performance tests
• Integrations to third-party tools like Jira

Cons

• No 24/7 live support available
• Can be expensive upfront

LoadView

LoadView is a cloud-based performance testing platform that provides real browsers for increased accuracy. It works for web pages, web applications, and APIs, and the system is fully-managed by LoadView, so sysadmins can focus on testing instead of maintenance. Users can design multiple test scenarios or have LoadView handle it through the professional services option. Companies can purchase the system monthly, yearly, or on an on-demand basis.

Key Features

• Load curves
• Real browsers
• Point-and-click scripting
• Customizable reports
• Globally-distributed load testing

Pros

• Helpful for testers with limited technical knowledge
• Free trial with credits available
• Helpful and responsive customer support with 24/7 live chat

Cons

• There can be a learning curve with the system
• Can be expensive compared to similar tools

Micro Focus LoadRunner

Micro Focus LoadRunner allows globally distributed teams to easily collaborate on performance testing. It’s available for both cloud and on-premises environments, with the cloud-based tool offering the ability to simulate over 5 million virtual users. Users can run multiple tests at the same time, and it offers a large number of integrations, including Kubernetes, Docker Swarm, Selenium, and AppDynamics. Pricing information is not available on the website.

Key Features

• No concurrency limits
• Cloud and on-premises options
• Integrations with any IDE or CI tool
• Sharable testing resources and scripts
• Shared and open architecture

Pros

• Works well with large workloads
• Supports a variety of protocols
• Easy to use with an intuitive user interface

Cons

• May be expensive compared to similar tools
• Configuration can be difficult in the beginning

NeoLoad

NeoLoad from Tricentis is an enterprise performance testing tool that helps organizations simplify and scale their software development processes. It works well for APIs and web services as well as full applications. The cloud-based platform integrates easily with any cloud development platform and offers an automated approach to reduce some of the burden on sysadmins. Pricing information is not available on the website.

Key Features

• No-code test creation
• Infrastructure monitoring
• Collaborative testing
• Virtual user emulations
• Version control

Pros

• Helpful reporting options
• Easily integrates with other software
• Intuitive user interface

Cons

• It’s a large application that can be resource-intensive
• Limited web support available

Rational Performance Tester

Rational Performance Tester from IBM allows systems and network administrators to test their products earlier and more frequently during the development process. It can identify the causes of slowdowns or bottlenecks in the software and integrates easily with other IBM products for full visibility into the development environment. Users can also create test scripts without programming, making testing easier. Interested organizations will have to contact IBM for pricing information.

Key Features

• No-code test creation
• Virtual user emulation
• Root-cause analysis
• Real-time reports
• On-premises and cloud-based options

Pros

• Integrates well with any IDE
• Good reporting options with sharing capabilities
• Easy to set up distributed load testing

Cons

• The platform has a steep learning curve
• Can be resource-intensive and consume a lot of memory

SmartMeter.io

SmartMeter.io is a performance testing tool that offers an embedded browser, making it easy for users to create and run test scenarios. Live test monitoring allows users to see how the program responds to changes in real-time, but there are also detailed reports available after the test has concluded. The system also automatically backs up test scripts and results of tests that users have already run. There are four subscription tiers available with prices starting at $39/month. However, yearly subscribers get the first two months free.

Key Features

• Detailed test reports
• Scenario recording
• Distributed load testing
• A variety of third-party integrations
• Real-time test monitoring

Pros

• Easy to use without a large learning curve
• Comprehensive test reports
• Load testing is scalable

Cons

• Initial configuration can be difficult
• The customer support is not very responsive

Apache JMeter

Apache JMeter is an open-source performance testing tool that can simulate heavy user loads and provides its own testing IDE. It works with a large variety of protocol types, including FTP, web, TCP, and Java Objects. Users can analyze results even while offline and record tests from their browser or native applications. Because Apache JMeter is open-source, it is free to use and download, although users can sponsor or donate to the program to aid in development.

Key Features

• Included IDE
• Virtual load simulation
• Unlimited testing
• Data analytics plug-ins
• Third-party integrations for Maven, Gradle, and Jenkins

Pros

• Supports a large variety of test types
• The user interface is easy to understand
• Open-source nature means it’s free to use and updated frequently

Cons

• Can be resource-intensive when testing large applications
• It sometimes runs slowly

Performance Testing Improves Customer Satisfaction & ROI

Performance testing tools help IT and dev teams catch issues early in the development process, meaning they won’t have as many customer complaints down the line. Additionally, because problems are easier to fix the earlier engineers catch them, they’ll improve the ROI on their product.

To find the right performance testing tool, organizations need to choose a platform that includes its own IDE or integrates with their dev team’s chosen IDE. Additionally, they’ll want to be able to run a variety of different tests and simulate large loads to see how the application holds up. Finally, businesses should consider performance testing tools with automated testing and artificial intelligence to reduce the workload on already overburdened employees.

Finished with the development process? Check out our Guide to Transitioning From Software Development to Maintenance.

The post Best Performance Testing Tools for 2022 appeared first on IT Business Edge.

Here are the essential elements to include in a business requirements document, plus best practices and scope limitations and considerations.

Also read: Best IT Project Management Tools & Software

Key Elements of a Business Requirements Document

Here are 10 elements to include in a business requirements document that will help assure your team’s success.

Versioning

A business requirements document is a living thing. It is created before a project starts, can change frequently, and may still be edited once everything else is finished.

Because the business requirements document will be referenced time and again, it’s important that all changes are noted within reason. If requirements or dates change, record it; if you fixed a typo, let it slide.

Summary statement

Even though the summary statement tends to appear first in a business requirements document, it’s recommended to be written last. It’s a high-level statement that should outline the project requirements and summarize the rest of the document.

Project objectives

Outline the project goals and objectives, detailing what the work will accomplish. If the project supports business processes or workflows, it should be described here.

Objectives should always be SMART—specific, measurable, attainable, realistic, and time-bound.

Needs statement

The needs statement is intended to be persuasive. It’s the reason for the project. Think of the needs statement as a justification meant to sell stakeholders on the idea and to motivate the project team.

Project scope

Detailing the project scope will help set boundaries for the work to be completed. Depending on your project, goals, team, and environment, it can sometimes be easier to identify items or modules that won’t be updated or included in the project scope instead of defining all the things that are.

Stakeholders

Identify all the stakeholders involved, including their positions. It is helpful to list their position within the organizations involved, but also their roles and responsibilities as they pertain to the current project.

Financial statement and cost-benefit analysis

Not to be thought of as a budget, the financial information included in a business requirements document is intended to indicate the impact of the project on a company’s balance sheet.

Funding sources should be identified here, but don’t forget that any person or organization contributing may also qualify as a stakeholder and should be included in both sections.

Schedule, timeline, and milestones

Depending on the project size, information for the schedule, timeline, and milestones may be combined into a single section or separated out into their own. It’s important to clearly identify expectations and deadlines, being sure to include decision points as well as moments when work needs to be completed.

Track any and all activities, including when you need to have sign-off on project deliverables, when outside vendors need to be engaged, and when hardware has to be in place.

For long-term projects, identifying clear milestones allows the ideal opportunity for interim billing, so vendors and contractors can be paid.

Functional requirements

Functional requirements make up the real bulk of a good business requirements document. The more detailed the requirements, the better the outcome.

Be sure to use clear, concise language free of jargon or slang. Avoid acronyms, even if they feel common. And when possible, add visual elements like screenshots, prototypes, and mock-ups. It’s a great idea to compare current state to future state when business processes or workflows are changing.

Where it makes sense, break large sections into smaller, more accessible pieces. And if requirements are optional or subject to other dependencies, break them down by must have, should have, and nice to have.

Non-functional requirements

Document any reporting, analytics, and integration requirements in this section. Be mindful that some activities, such as security scans, may necessitate revisiting other sections of the business requirements document, and time should be budgeted for accordingly.

Also read: How Project Management Software Increases IT Efficiency

Business Requirements Document Best Practices

There are a number of best practices that can ensure your document – and project – will be a success. Here are 8 to consider as you plan your project.

• Get input and perspective. Subject your business requirements document to peer review.
• Set reasonable deadlines. Double and triple check dates and deadlines to be sure they are achievable—it’s better to estimate high and deliver early than have projects fall behind.
• Include time for research. If contractors or vendors need to be engaged, be certain the time and costs of doing so are included. If researching a needed vendor or third-party product is a part of the project, identify it as a risk to mitigate in case an appropriate solution isn’t found, takes longer than expected, or exceeds the budget allowance.
• Be aware of regulatory requirements. Don’t forget to account for any regulations or legislation that may impact the project.
• Detail needed technology. Include details on the tools and technology that will be used and employed.
• Plan for ongoing support. If your project will require ongoing maintenance and support after implementation, specify a support plan, and list the activities and individuals involved.
• Leave time for documentation. Remember that documentation should be a part of any project. Activities should be included with time allotted to complete documentation and training materials.
• Be flexible. Stay open to identifying and evolving functional requirements, but remain aware of how changes may impact other activities, timelines, or deadlines.

Limitations of Business Requirements Documents

Despite being a source of truth and trusted advisor for a project, business requirements documents do have their limitations. Here are some of the limitations of the document’s scope and how to navigate around them.

• You don’t always need to know how something gets done. Functional requirements should answer questions of what and why but not how. Though the distinction may feel subtle, knowing how a developer will accomplish a particular task is outside the scope of these documents.
• Don’t leave questions unanswered. Business requirements documents should always answer questions, not ask them. If there are questions to be asked, or unknowns to research, do so during the creation of the document, and include the results instead.
• Include all background and details. Each business requirement document should stand alone. Assume that everybody reading it has no idea what has happened in past projects. If there are details that need to be included to offer context, include them, but be sure they are relevant and necessary.
• Plan for delays. Though few business requirements documents include a risk mitigation section, it’s wise to find ways to identify areas where timelines or activities could be impacted and in what way. A rule of thumb is to add a 20% time buffer to manage uncertainties, but adjust this as needed and appropriate.

Business Requirements Documents Inspire Teamwork

When done with care and consideration, a business requirements document fosters trust and transparency among project teams and collaborators. Communications are improved, there are fewer errors and mistakes, ambiguities and uncertainties are reduced or eliminated, and outcomes can be all but guaranteed.

Read next: Choosing Between the Two Approaches to Project Management

The post Tips for Writing the Perfect Business Requirements Document appeared first on IT Business Edge.

Why is Data Quality Important?

The success of many businesses today is impacted by the quality of their data, from data collection to analytics. As such, it is important for data to be available in a form that is fit for use to ensure a business is competitive.

Quality data produces insights that can be trusted, reducing the waste of organizational resources and, therefore, impacting the efficiency and profitability of an organization. Maintaining high data quality standards also helps organizations satisfy different local and international regulatory requirements.

How do Data Quality Tools Work?

Data quality tools analyze information to identify obsolete, ambiguous, incomplete, incorrect, or wrongly formatted data. They profile data and then correct or cleanse data using predetermined guidelines with methods for modification, deletion, appending, and more.

Also read: Data Literacy is Key for Successful Digital Transformation

Best Data Quality Tools & Software

DemandTools

DemandTools is a versatile and secure data quality software platform that allows users to speedily clean and maintain customer relationship management (CRM) data. It also provides users with correct report-ready data that boosts the effectiveness of their revenue operations.

Key Differentiators

• Data Quality Assessment: Through the Asses module, DemandTools helps users recognize the degree of strength or weakness of their data to determine where they should focus remediation efforts. Unactionable, Insufficient, Limited, Acceptable, and Validified are five data quality categories which allow users to understand the overall state of their data.
• Duplicate Management: DemandTools helps its customers to discover, remove, and prevent duplicate records from misleading various teams within the organization, thus complicating their customer journeys. Duplicate management happens through modules such as Dedupe, which cleans up existing duplicates; Convert, which keeps lead queues duplicate-free; and DupeBlocker, which is a Salesforce duplicate blocker.
• Data Migration Management: DemandTools ensures the integrity of data is maintained as it enters and exits Salesforce. It uses modules such as Import, Export, Match, Delete, and Undelete.
• Email Verification: Users can verify email addresses in their CRM to ensure they have an effective line of communication with their customers. And lead and contact email addresses can be verified in bulk.

Con: A majority of the tool is designed around Salesforce.

Pricing: Base pricing begins at $10 per CRM license. You can contact the vendor for a personalized quote.

Openprise

Openprise is a no-code platform that empowers users to automate many sales and marketing processes to reap the value of their revenue operations (RevOps) investments. As a data quality tool, Openprise allows users to cleanse and format data, normalize values, carry out deduplication, segment data, and enrich and unify data.

Key Differentiators

• Openprise Data Cleansing and Automation Engine: Openprise ensures data is usable for users’ key systems through aggregation, enrichment, and transformation of data. Openprise’s focus goes beyond sales systems to offer flexibility to their customers. Integration with users’ marketing and sales systems enables Openprise to push clean data and results to these systems to deliver greater value.
• Openprise Bots: Users can deploy automated bots to monitor and clean data in real time to ensure data is always in the best condition.
• Normalized Field Values: Data is normalized to customers’ specifications to smoothen segmentation and reporting. It standardizes company names, phone numbers, and country and state fields among others.
• Deduplication: Users can dedupe contacts, accounts, and leads. It has prebuilt recipes designed involving best practices users can take advantage of. They can also modify dedupe logic to customize the deduplication process to their needs.

Con: The user interface (UI) can be overwhelming, especially to new users.

Pricing: The Professional package starts at $24K per year for up to 250K records. For the Enterprise package and further pricing information, contact Openprise.

RingLead

RingLead is a cloud-based data orchestration platform that takes in data from many sources to enrich, deduplicate, segment, cleanse, normalize, and route. The processes help to enhance data quality, set off automated workflows, and inform go-to-market actions.

Key Differentiators

• RingLead Cleanse: RingLead Cleanse detects and removes duplicates in users’ data through proprietary duplicate merging technology. Users can clean CRM and marketing automation data through deduplication of people, contacts, leads, etc. RingLead Cleanse can also link people to accounts, normalize data structure, segment data into groups, and get rid of bad data.
• RingLead Enrich: The purpose of RingLead Enrich’s data quality workflow engine is to be the central point of users’ sales and marketing technology stack. Users can configure batch and real-time enrichment into their sales and marketing and data operations workflows. They can also integrate their internal systems and data ingestion processes with third-party data sources, optimizing ROI from third-party data enrichment.
• RingLead Route: Users can achieve validation, enhancement, segmentation, normalization, matching, linking, and routing of new leads, accounts, opportunities, contacts, and more in one flow, making RingLead a fast and accurate lead routing solution.

Con: The UI has a learning curve.

Pricing: Contact RingLead for custom pricing information.

Melissa Data Quality Suite

Melissa Data Quality Suite combines address management and data quality to ensure businesses keep their data clean. Melissa’s data quality tools clean, rectify, and verify names, phone numbers, email addresses, and more at their point of entry.

Key Differentiators

• Address Verification: Users can validate, format, and standardize the addresses of over 240 countries and territories in real time to prevent errors such as spelling mistakes, incorrect postal codes and house numbers, and formatting errors.
• Name Verification: Global Name identifies, genderizes, and parses more than 650K ethnically diverse names using intelligent recognition. It can also differentiate between name formats from different languages and countries and can parse full names, handle name strings, and flag vulgar and fake names.
• Phone Verification: Melissa Global Phone can validate callable phone numbers, determine their accuracy for the region, and verify and correct phone numbers at their point of entry to ensure users populate their databases with correct information. It also ensures the numbers are live and identifies the dominant languages in numbers’ regions.
• Email Verification: To prevent blacklisting and high bounce rates and to improve deliverability and response rates, Melissa Global Email Verification carries out email checks to fix and validate domains, spelling, and syntax. It also tests the SMTP (Simple Mail Transfer Protocol) to globally validate email addresses.

Cons: Address updates could be more frequent, and address validation can be resource-intensive and time-consuming.

Pricing: Base pricing is at $750 per year for 50K address validations. Contact Melissa for a free quote.

Talend

Talend Data Quality ensures trusted data is available in every type of integration, effectively enhancing performance and bettering sales while reducing costs. It enriches and protects data and ensures data is always available.

Key Differentiators

• Intuitive Interface: Talend Data Quality cleans, profiles, and masks data in real time, using machine learning to support recommendations for handling data quality matters. As a result, its interface is intuitive, convenient, and self-service, making it effective for not only technical but also business users.
• Talend Trust Score: The built-in Talend Trust Score provides users with instant, explainable, and actionable evaluations of confidence to separate cleansed datasets from those that need more cleansing.
• Talend Data Quality Service (DQS): With Talend DQS, organizations with limited data quality skills, talent, and resources can implement data quality best practices up to three times as fast as they would have by themselves. Talend DQS is a managed service that helps users constantly monitor and manage their data at scale as well as track and visualize data quality KPIs (key performance indicators).
• Asset Protection and Compliance: To protect personally identifiable information (PII) from unauthorized individuals, Talend Data Quality allows users to selectively share data with trusted users.

Cons: It can be memory-intensive.

Pricing: Contact Talend Sales for more information on pricing.

WinPure Clean & Match

WinPure Clean & Match carries out data cleansing and data matching to improve the accuracy of consumer or business data. This data quality tool features cleaning, deduplicating, and correcting functions ideal for databases, CRMs, mailing lists and spreadsheets among others.

Key Differentiators

• WinPure CleanMatrix: WinPure CleanMatrix gives users an easy yet sophisticated method to carry out numerous data cleaning processes on their data. It is divided into seven parts, with each part responsible for a data cleansing task.
• One-Click Data Cleaning Mode: Clean & Match has a one-click data cleaning feature that processes all the clean options across various columns simultaneously.
• Data Profiling Tool: The data profiling tool scans each data list and gives more than 30 statistics. It uses red and amber to highlight potential data quality issues like dots, hyphens, and leading or trailing spaces. These issues can be fixed with a single click.

Cons: It has a learning curve.

Pricing: It features a free version, but base pricing starts at $999 per license for one desktop for the Small Business package. For Pro Business and Enterprise packages, contact the vendor.

How the Data Quality Tools Compare

Choosing a Data Quality Tool

Before selecting a data quality tool for your use case, it is important to consider your data challenges. Implementing a solution that partly or barely addresses your data challenges results in ineffective data management initiatives and impacts overall business success.

It is also important to understand the scope and limits of data quality tools to ensure they are effective. You should also consider the differentiators and weaknesses of the tools in consideration and align them with your goals. Finally, use free trials and demos where available for a hands-on experience.

Read next: Top Data Mining Tools for Enterprise

The post Top Data Quality Tools & Software 2022 appeared first on IT Business Edge.

Software Development Life Cycle (SDLC) Overview

The SDLC is a methodology with clearly defined processes that support the creation and upgrading of software applications.

The modern SDLC contains seven stages:

1. Planning: This is where the ideas flow and the excitement begins. Teams define problems, brainstorm solutions, and start to think of their development objectives.
2. Requirements Gathering and Analysis: In the second stage, teams define requirements and determine the specific details for the new development. It’s important to consider the needs of end users, integration with existing and ancillary systems, and need-to-have versus nice-to-have features.
3. Design and prototyping: Using tools like process diagrams and prototypes, development teams work to create the kinds of plans that will kick off and fuel the development phase.
4. Development: In this phase, code is written, and the software application takes shape.
5. Testing: This phase involves activities such as looking for bugs, finding defects, and learning about shortfalls.
6. Implementation and integration: Often thought of as the final phase of the SDLC, implementation and integration involves a number of tasks that take place as a new or updated software application moves to a live production environment. In addition, user training plans are executed and hardware is installed.
7. Operations and maintenance: After a software application makes the move to production, ongoing operations and maintenance begins. This involves ensuring end users are well-supported as well as identifying the need for patches and updates, which can be a catalyst that starts the SDLC all over again.

Also read: Steps to Conducting a Software Usability Test

Four Types of Software Maintenance

Corrective

Corrective software maintenance is the process that keeps an application up and running. Corrections are most often identified by end users and relate to the design, logic, or code.

Adaptive

Changes to your environment can have an impact on the software applications that run within it. This may be related to hardware updates, operating system updates, or changes to infrastructure. Environmental changes can also include vendor changes, connections to new or existing ancillary systems, or even policy related to security or industry compliance.

Perfective

Perfective software maintenance changes are typically evolutionary. As end users get to work with a software application, they start to create wish lists with new features. In some cases, removing unnecessary or redundant features is also a function of perfective software maintenance.

Preventative

Preventive software maintenance is similar to a technical bandage. It involves smaller, incremental, changes necessary to adapt software applications, so they can work for a longer period of time.

Also read: Using Swim Lane Diagrams to Improve Software Development

Best Practices: Transferring a Project From Development to Maintenance Teams

Transferring a project from a development team to a maintenance team can be complex and challenging, no matter the size. Fortunately, there are a few best practices that should be followed for all transitions.

Identify team leaders

Leaders from the project team, which may include development leads, business analysts, and other stakeholders, should be identified and kept in touch with maintenance team leaders. Knowing who to look to for decisions and guidance can help mitigate risk and ensure a smooth transition.

Team leaders should discuss whether the new software application will impact or change the existing SLAs (service level agreements).

Budget for the transition

Don’t forget to include transitioning from development to maintenance in your project budget. This process shouldn’t be rushed or an afterthought. Be sure stakeholders understand the importance of a proper support plan.

This budget may also include the need to add additional support staff that will be needed post-implementation.

Start Early

Avoid the drop-and-run approach to transitioning projects from development to maintenance. Allow maintenance teams to shadow development teams long before things are complete, include maintenance teams in meetings and relevant correspondence, and update everyone on important decisions.

By including maintenance team members early on, development teams will also have the opportunity to better understand the current state of existing architecture and current software applications being used by an organization.

Communication

Don’t forget the maintenance team may not understand why decisions were made, priorities were defined, or how needs were identified. By communicating these types of details, maintenance teams can better support the software application as well as have empathy and ownership when they need to respond to future questions from end users.

Documentation

Documentation is a critical part of the support process. Skilled technology professionals learn to intuit the details that should be documented to help guide support tasks later on. Think about end users that may look for justifications for the way features or functionality were implemented, and consider the reasons why decisions were made.

A secondary benefit of thorough documentation will be felt with future development efforts. Don’t assume updates or bug fixes will always be done by the same developers.

Documentation elements to include:

• Overview
• References
• Assumptions
• Contacts
• Agreements and licensing
• Diagrams and prototypes with functional and feature lists and summaries
• Configuration details, such as directory structure and administrative functions
• Operational details like start up, shut down, backup, recovery, and archiving
• Security details

Knowledge transfer

Documentation alone isn’t enough, though it is a part of the knowledge transfer process. The trick is to understand and respect the roles of everybody on each team, knowing each has subject matter expertise that may not be known by the other.

Encourage questions and ongoing conversations. There may be things the others haven’t thought of or third-party systems that may unknowingly impact or be impacted by a proposed implementation.

Both teams are important

Neither team is lesser than. Having respect for the work done by each team will help to see the value in the service each provides.

Overlap

Whenever possible, be sure the transition process includes time in the schedule for a little overlap. As support requests start to filter in, it can be helpful to have a resource the maintenance team can call on to get advice and assistance.

That said, be sure the time period for this service is clearly defined and communicated. Having a clear line drawn assists with feelings of ownership and allows both teams to properly move forward.

Learn From Each Transition

Even though every software development project is different, with varying scope and complexity, the transition process can be standardized and learned from. Maintenance teams should conduct post-implementation and transition meetings to discuss lessons learned and solidify best practices.

Document the questions you wish you had asked and timeframes you wish you had designed differently, and bring this knowledge and experience forward to the next transition.

Read next: How to Choose a Software Development Methodology: 6 Approaches

The post Guide to Transitioning From Software Development to Maintenance appeared first on IT Business Edge.

In 1992, the author of several mind-bending sci-fi novels, Neal Stephenson, gave this concept a name: Metaverse. Companies like Valve, Oculus, and now Facebook (rebranded as Meta) have chased this dream with mixed success, and in the latter’s case, some controversy.

One of the limiting factors of virtual reality’s (VR) success has been its technological maturity; however, with recent development of 5G and the metaverse, VR seems to be following a similar path as the iPhone.

While it wasn’t the first of its kind, Apple’s flagship smartphone offered an attractive and overall useful package to consumers, making it a success, especially in its second generation with the support of the 3G cellular network. The drive toward mobile data usage and the technologies deployed by major U.S. telecommunications companies pushed smartphones into ubiquity.

Similarly, recent telecommunications technologies seem to be pushing virtual reality into rising popularity. With 5G hitting the airwaves, brand new bandwidth is opening up, leaving the telecommunications industry wondering what the next app that will take advantage of this new capacity is. Their answer is the metaverse.

Also read: What is the Metaverse and How Do Enterprises Stand to Benefit?

Virtually Everything

Verizon foresees a future where virtual reality and augmented reality (AR) are as commonplace as smartphones are now, enabled by a massive increase in data transfers from a nationwide 5G network.

As they describe it, metaverse will transcend beyond gaming and open up new possibilities, such as allowing shoe shoppers to use AR to try on a pair of virtual sneakers or cosmetics before buying the real thing.

The trick is, they don’t just want to deliver the experience; they want to sell the experience, too. As such, Verizon is putting real money behind this, launching metaverse experiences such as a fully virtualized Super Bowl.

And they aren’t alone. China Mobile kicked off its Mobile Cloud VR last year, which is a virtual socialization and shopping app supported by 5G. In addition, SK Telecom recently launched its own metaverse platform.

These companies saw the profits Apple and Google swept by leveraging 3G and 4G advancements, and they seek to get ahead of everyone else by planting their flags in the VR/AR space with their own apps.

How to Experience the Metaverse

High-quality virtual reality and augmented reality experiences can be had right now, but they come with significant limitations. An Oculus Quest 2 is a powerful device that costs less than what people pay for cell phones every year, but all that hardware is packed into an awkward, weighty package that can cause discomfort during prolonged play sessions.

The ill-fated Google Glass promised to bring maps, your calendar, the weather, and a host of other augmented reality services right before your eyes wherever you go. Despite an interesting premise, the product never found its footing, though Google hasn’t given up on it yet.

The right formfactor to experience a metaverse has yet to emerge, it seems.

Also read: The Metaverse: Catching the Next Internet-Like Wave

What’s the Real Vision Here?

Nevertheless, 5G providers like SK Telecom remain optimistic. The company’s vice president Cho Ik-hwan has even commented that the metaverse will become their core business platform as they develop first-party applications meant to occupy what they see as a wide-open space.

“We want to create a new kind of economic system,” said Ik-hwan. “A very giant, very virtual economic system.”

It’s unclear how SK Telecom will achieve that goal. At present, the company and others like it are investing in the development of VR/AR smartphone apps, but a cell phone with a 6-inch display screen doesn’t seem like an attractive formfactor to experience a transcendent metaverse adventure.

Further, the concept of a metaverse is still vague and formative, and even a $10 billion investment from Facebook has yet to give it focus or profit.

Similarly, Verizon’s approach seems unfocused, even self-contradictory. The company promises their metaverse experience will be without limits in a sentence immediately following a statement that you will “be required to abide by rules and regulations just like you would in the real world.”

That type of thinking exposes the real challenge telecommunications faces on this frontier. In this endeavor, they are stepping well outside their existing business models of steadily building infrastructure and entering into a field that demands artistic creativity and dynamism.

That field is more compatible with the “move fast and break things” mentality of Silicon Valley, and even Facebook is fighting an uphill battle.

Read next: Emerging Technologies are Exciting Digital Transformation Push

The post Is 5G Enough to Boost the Metaverse? appeared first on IT Business Edge.

Whether a team uses whiteboards to brainstorm, demonstrate an answer, diagram a solution, or talk through complexities with requirements, whiteboards can be a valuable collaboration tool.

Understanding Whiteboards

Simply stated, whiteboards are visualization tools. Content added to a whiteboard can be as permanent or temporary as each situation requires.

Online whiteboards bring this process to a digital space, providing the opportunity for collaboration amongst team members that don’t share the same physical space as well as providing shared documentation that can be more easily accessed for future use.

In addition, whiteboards are intended to be organic, inviting users to participate in content creation and the resulting discussion. Other functions of whiteboards include the ability to:

• Brainstorm, explore, and share ideas.
• Solve problems, strategize, and perform analysis tasks.
• Create high-level design diagrams to walk users through functionality or proposed solutions.
• Plan projects and resources.
• Teach and learn new concepts.
• Plan sprints and group tasks.

Whiteboard Coding

In the development process, it can be helpful to temporarily abandon the constraints of an IDE (integrated development environment) and think through the necessary logic required. Taking a step back from strict syntax and formatting can offer fresh inspiration on how to tackle the next stages of development.

In more complex situations, illustrating the situation can make it easier to solicit feedback or advice, particularly from team members who may be familiar with the project but are not also developers.

Whiteboards should supplement and support development efforts, and care should be taken to ensure they don’t negatively impact productivity. Whiteboards aren’t intended to be a primary coding method. 

Also read: The Importance of Usability in Software Design

Common Whiteboard Application Features

Whiteboard applications share a number of features, including:

• Taking the form of a native application (desktop or mobile) or being browser-based.
• Unlike physical whiteboards, space isn’t limited and can scale as needed.
• Depending on the device being used, content can be added using a keyboard, mouse, digital pen, stylus, or fingertip.
• Whiteboards can include shapes, pictures, images, and other interactive media content.
• Participants are able to select, move, resize, edit, and delete content.
• Voting sessions allow participants to decide on the importance and priority of various ideas, features, or functionality.
• Commenting allows contributors to provide feedback, introduce additional questions or considerations, or to encourage additional discussion.

Also read: Using Swim Lane Diagrams to Improve Software Development

Top 5 Whiteboard Applications

There are a large number of whiteboard applications available, but five stand out as the top choices.

Note that the costs included below may not be inclusive of the price for applications needed by mobile or desktop devices when not accessing the whiteboard using a web browser. 

Miro

Pros:

• Miro includes several useful templates to get you started.
• The versatile user interface (UI) allows organizations to customize their whiteboards to best suit their processes, workflows, and objectives.
• Hand-written notes can easily be integrated into your whiteboards.

Cons:

• With the considerable number of features and functionality offered by Miro, the UI can feel cluttered and confusing to new users.
• Administrative functions such as moving boards between teams can be difficult.
• Large boards can be slow to load to the viewer’s screen.

Platform Compatibility: 

• Miro is compatible with Mac, Windows, iOS, and Android.

Cost:

• Basic plans are free.
• Team features are available with plans starting at $16 per user, per month.
• Enterprise plans are available with custom pricing and include enterprise-grade security and compliance, centralized account management, and additional data governance functionality.
• Plans paid annually receive a 20% discount.

Microsoft Whiteboard

Pros:

• The familiar Microsoft layout and interface design reduces the learning curve.
• The ability to easily embed other Office documents and work seamlessly within Teams makes Microsoft Whiteboard ideal for organizations using the whole 365 ecosystem.
• The pen experience within Microsoft Whiteboard is applauded as being the most like an actual pen.
• The artifact is not lost when a whiteboard is shared, making it easy to go back and make revisions or additions.

Cons:

• Microsoft Whiteboard often tries to turn ink into computer text, which isn’t always ideal.
• For organizations with users accessing whiteboards using a variety of endpoints and platforms, Microsoft Whiteboard doesn’t always provide an equivalent experience for everyone.
• There is a lack of useful connectors for products like Visio.

Platform Compatibility:

• Microsoft Whiteboard is compatible with Windows, iOS, and Android.

Cost:

• Microsoft Whiteboard is free with a Microsoft account, and additional functionality is available with a Microsoft 365 subscription.

MURAL

Pros:

• MURAL integrates easily with the software already being used by your organization, including Teams, Asana, Azure AD, Adobe Creative Cloud Library, and more.
• Users can easily work with collaborators in real time or asynchronously.
• Projects can be started using templates.
• Interactive elements allow for things like summoning collaborators back after a break.

Cons:

• The visual interface tends to lag when larger numbers of users are making modifications simultaneously.
• Private boards require premium plan subscriptions.

Platform Compatibility:

• MURAL is compatible with Mac, Windows, iOS, and Android.

Cost:

• The Free plan is best for organizations only needing a maximum of 3 whiteboards.
• Unlimited whiteboard plans with additional privacy controls for teams are available for $9.99 per user, per month.
• Business plans with SSO and advanced integrations (Jira and GitHub) are available for $17.99 per user, per month.
• Enterprise plans with centralized administration and enhanced security are available with custom pricing.

Explain Everything

Pros:

• Explain Everything is celebrated for piecing whiteboard content together into explainer videos, making it ideal to use as an educational tool.
• Whiteboards are designed to be more of a teacher-student classroom-style collaboration tool, with built-in functionality for guiding others through lessons or content.

Cons:

• The drawing features are not as sophisticated as those found in competing whiteboard applications.
• While the user interface makes easy tasks very easy, more complex tasks are almost impossibly difficult.

Platform Compatibility:

• Explain Everything is compatible with Mac, Windows, iOS, and Android.

Cost:

• The basic single-user plan is free, and team plans begin at $11.99 per user, per month.
• Education plans are also available for students, teachers, and other educators.

Zoom

Pros:

• The whiteboards are available to all Zoom users who are already engaged in a familiar, collaborative, online meeting application.
• Finished whiteboards can be saved as images and shared.

Cons:

• Whiteboards can only be initiated by the meeting host, who can then transfer the sharing rights to other participants.
• The function is only intended for brief collaboration sessions during Zoom meetings and not ongoing, long-term whiteboarding.

Platform Compatibility:

• Zoom is compatible with Mac, Windows, iOS, Android, and Linux.

Cost:

• The cost for whiteboard functionality is included with Zoom membership pricing.

Tips for Using Whiteboard Applications

There are as many ways to use a whiteboard as there are items pinned to your tried-and-true bulletin board. That said, there are a few best practice tips and tricks that should be considered. 

• Practice makes perfect. Feeling empowered and free to contribute to a whiteboard is an expertise that takes time to develop.
• Give your content meaning. If you want something to stand out, write it in red. If you want to show the flow of a process or idea, use an indicative arrow. Taking some time to standardize your whiteboarding strategy will pay off with more meaningful and easier to understand whiteboards.
• Make good use of space. Knowing users may be accessing and contributing to your whiteboards using a variety of devices and endpoints, means screen sizes and resolutions can vary tremendously. Make choices regarding font and shape size carefully, and try to minimize the need to scroll for the most important information.
• Brainstorm first, edit later.

Turning Ideas into Tasks

There are reasons why so many offices have physical whiteboards hanging on the walls, usually covered in sketches and jot notes that are tied together with lines and arrows, sometimes circled, often with question marks or additional notes added on later. They may even have a sticky note or two. 

Unfortunately, these physical whiteboards have limitations. Even when you disregard their need for members to be in the same location, content isn’t dynamic. You can’t add screenshots, images, or other multimedia. Everything must be recreated or described. Plus, it can be difficult enough to organize our own thoughts, let alone share them in a clear and meaningful way with others.

By contrast, whiteboard applications are always accessible, living things. We can ask questions and see how they get sketched out. We can send our creations to peers and experts for advice or suggestions. We can make note of things to remember or add reference points for later consideration.

Whiteboards don’t have the permanence of documentation. They don’t need to be beautifully formatted or proofread. They often come with the understanding that they may not even be technically accurate or current. They are collaborative tools, allowing teams to think out loud together in a functional way.

Read next: Identifying Software Requirements Using 5 Whys and 5 Hows

The post Using Whiteboards to Streamline Development Team Collaboration appeared first on IT Business Edge.

It features a flexible use of resources that can be scaled based on real-time requirements. As a result, it is favored by DevOps thanks to its quicker development lifecycle.

How Does Serverless Computing Work?

Serverless computing provides provisioning, scheduling, scaling, and other back-end cloud infrastructure and operations tasks to the cloud provider. As a result, developers get more time to develop front-end applications and business logic. This eases up on the workload of your teams and ensures their maximum focus is on innovation.

Though technically, it does use servers, it is called serverless computing because the servers, in this case, are hosted by a third-party service provider, making them seemingly non-existent to the customer, who is not responsible for managing them. This is an essential step towards NoOps (no operations).

Every major cloud service provider offers a serverless platform, including Microsoft Azure, Google Cloud, and Amazon Web Services. Serverless is also at the core of cloud-native application development.

Also read: Is Serverless Computing Ready to Go Mainstream?

How are Serverless Platforms Improving DevOps?

The serverless model is better suited for certain customers than the IaaS and SaaS models, which demand a fixed monthly or yearly price. Sometimes developers do not need to use the entire capacity offered by their cloud solution.

In such cases, serverless computing provides a fine-grain, pay-as-you-go model, so you only pay for the resources consumed for the life of the called function. This can lead to significant reductions in the projected cost, allowing for greater savings over other cloud models for many workloads.

However, serverless models can only be considered an evolving technology at best. Considering it as a universal solution for development and operations problems, this model can lead to certain drawbacks.

That being said, IT professionals have reported using serverless for a large array of applications, including customer relationship management (CRM), finance, and business intelligence.

Popular Applications of Serverless Computing

Many major cloud service providers offer serverless platforms to users who can finally enjoy a NoOps state, including Amazon, Google, and Microsoft. Serverless platforms by Alibaba, IBM, and Oracle, among others, are soon to follow. At the same time, open-source projects such as OpenFaaS (function as a service) and Kubeless are bringing serverless technologies to on-premises architecture. 

Get support for microservice architecture

One key usage of serverless computing is its support for microservice architectures, which enable the creation of small services with a singular job that can use APIs to connect to one another.

Serverless is uniquely suited for this model, which needs to run code that supports automatic scaling. Plus, the pricing model functions in such a way that you aren’t charged when no operations are running as opposed to PaaS or containers.

Also read: Securing Your Microservices Architecture: The Top 3 Best Practices

Work with different file types

Serverless works perfectly well with files in most formats such as video, image, text, or audio. You can carry out various functions such as data transformation and cleansing. In addition, text processing, such as PDF processing, sound manipulation like audio normalization, or video and image processing is also possible.

Compute parallel tasks

Any parallel task presents an excellent example for serverless runtime, and each parallel task triggers one action. Such tasks may include searching and sorting objects stored on the cloud, such as web scraping or map operations. Further, you can perform complex tasks like business process automation or hyperparameter tuning.

Robust foundation for streaming applications

Using FaaS, it is possible to build a steady foundation for the real-time creation of data pipelines and streaming apps. It is compatible with all kinds of data streams, including log data for IoT and other applications, validation, cleansing, enrichment, and transformation.

Test service continuity

You can set up FaaS, such as AWS Lambda functions, to make API calls to your services, much like API calls made by users. You can even create a mock flow of traffic to the services in production using FaaS.

These are good practices to test your service continuity periodically. Any failures that you might encounter are visible in your monitoring tool, so you are aware of failures or any performance drops.

Serverless pipelines for continuous deployment

You can use serverless to improve the CI/CD (continuous integration and continuous delivery) process and automate the entire process, from merging pull requests to deploying in production. And since FaaS functions are cost-efficient and easy to set up, DevOps engineers can focus on other parts of the infrastructure and further reduce costs.

Also read: Effectively Using Low-Code/No-Code in the Developer Cycle

Advantages of Using DevOps with Serverless Computing

Serverless computing has the potential to transform IT operations. By extending its property of levying charges based on function calls, developers can enjoy several applications. Some of the other major benefits of serverless computing include:

• Infinite scalability: Serverless computing allows you to scale functions horizontally and elastically based on the user traffic.
• NoOps: Infrastructure management in serverless computing is completely outsourced, so your in-house teams only need to deal with the operational tasks.
• No idle time costs: Legacy cloud computing models charge you per hour for running virtual machines. With a serverless computing model, you only need to pay for the execution duration and the number of functions executed.

Drawbacks of Serverless Computing

Serverless computing has enabled a range of operations, and organizations can run many different applications on it. However, it might not be the best choice for specific applications, which leads to a few possible disadvantages, including:

• Stable or predictable workloads: Serverless offers the most cost-effective model for unpredictable workloads. However, steady workloads with predictable performance requirements do not need this feature. Instead, traditional systems suit it better as they are much simpler and can be cheaper than serverless in such cases.
• Cold starts: Serverless architectures are optimized for scaling up and down to zero, but not long-running processes, meaning they might be starting up from zero for a new request. This might cause a noticeable startup latency, which might not be acceptable to specific users.
• Monitoring and debugging: A serverless architecture aggravates the complexity of the already challenging operational tasks. For example, debugging tools are typically not updated to meet the requirements of serverless computing.

The Future of Serverless Computing in DevOps

Serverless computing works uniquely well with DevOps, opening a vast array of applications faster at lower cost and complexity of architecture. Developers rely on it for its various functions which offer several unique features.

The concept of serverless computing is constantly evolving to solve more and more development and operational challenges. Though there are certain challenges that need addressing, the tools and strategies in serverless will eventually adapt to serve DevOps better. Today, most major cloud service providers are betting on serverless, and one can expect better-optimized solutions in the future.

Read next: Best DevOps Monitoring Tools for 2022

The post Improving DevOps with Serverless Computing appeared first on IT Business Edge.

These two sets of questions can also assist with eliciting the right software requirements by drilling down to identify needed and necessary functionality.

Also read: How to Choose a Software Development Methodology: 6 Approaches

Understanding How To Use the 5 Whys and 5 Hows

The trick with these techniques is to keep the questions simple and avoid influencing the answers. In almost all cases, the stakeholders or users you are speaking with will learn as much as you do.

Don’t be concerned if you need more than 5 questions to get to the answers you need; you may also be able to get there with fewer questions.

Using 5 Whys: An Example

As you might expect, the concept is simple: repeatedly ask the question, Why? until you get to the real root of the issue. As you get answers, try parroting them back in each iteration.

As an example:

1. Business Analyst: Why have you requested an update to your client invoicing software application?

Stakeholder: Because it is taking too long for clients to receive invoices for work we have completed.

2. Business Analyst: Why is it taking too long for clients to receive invoices for the work you have completed?

Stakeholder: Because timesheets aren’t being approved on time.

3. Business Analyst: Why aren’t timesheets being approved on time?

Stakeholder: Because HR managers aren’t receiving them from employees on time.

4. Business Analyst: Why aren’t HR managers receiving timesheets from employees on time?

Stakeholder: Because all timesheets are currently being entered by a single clerk in our office.

5. Business Analyst: Why are all timesheets currently being entered by a single clerk in your office?

Stakeholder: Because the current method of submitting timesheets is non-standardized and a primarily manual process.

What we learned through these five questions is that we can likely improve or solve the problem being faced by our stakeholder by automating timesheet entry and approval functionality. Don’t forget that this is often a cyclical process and can be repeated as often as is necessary. Extending this example, you may want to dive deeper into how you could standardize the current processes, looking into finer details such as deadlines.

Though not detailed enough on their own, keep phrases in mind like: Why is this important? Why would this help? Why haven’t you already done this? Why is it done this way?

Using 5 Hows: An Example

Sometimes the need for 5 Hows follows 5 Whys, but not always. It can also be helpful to determine how to proceed in a given situation, or how to problem solve and find solutions to IT-related issues.

As an example:

1. Business Analyst: How did the software application become unavailable?

Stakeholder: We were the victims of a malware attack.

2. Business Analyst: How did you become the victim of a malware attack?

Stakeholder: We haven’t implemented any security monitoring processes or solutions.

3. Business Analyst: How is it possible that you have not implemented any security monitoring processes or solutions?

Stakeholder: We haven’t had the budget approved for procuring the necessary technology.

4. Business Analyst: How do you proceed with getting the budget approval for the necessary technology?

Stakeholder: We need to hire for the security manager position.

5. Business Analyst: How do you hire for the security manager position?

Stakeholder: We need to discuss the impact of this recent malware attack with our executive team.

In this case, we were able to determine that the stakeholder is already aware of the issue, but they hadn’t clearly mapped the path to a solution. This may not be the end of your interrogation, but it’s delivered the first necessary answers. Don’t be afraid to start broad and vague with your 5 Hows, using future iterations as a means to determine priorities, set deadlines, define scope, or to see if smaller improvements could make a difference.

Also read: Using Swim Lane Diagrams to Improve Software Development

A Judgment-Free Process

It’s important that any facilitator asking why and how questions doesn’t make participants feel they are being judged. The goal is to dig deeper, learn more, and understand better.

Try to avoid getting frustrated. Stakeholders often don’t know exactly what they need, or what would alleviate their pain points. Show appreciation for all answers given during an interview and consider rephrasing or restating questions that may have been misunderstood.

Your job is to better understand a situation or test out your ideas for solutions. Don’t make assumptions. 

The Three-Legged 5 Whys or 5 Hows

In many situations, any given request or problem may have several contributing factors. If your first interview reveals that there are additional considerations, don’t be afraid to conduct several interviews. As mentioned, these two processes should be considered iterative and therefore could also be ongoing.

Emphasizing Collaboration

Ideally, the result of your interrogation will provide the basis for an action plan. In some instances, the answers to your questions may identify that stakeholder needs can be addressed by better utilizing existing software applications or technologies, making education the only requisite response.

Ultimately, the goals of 5 Whys and 5 Hows is collaboration. Whether you are realizing a new opportunity, helping to justify a software development activity, or just trying to better understand business processes and workflows, having a better understanding will always translate into better requirements.

Read next: 10 User-Centered Software Design Mistakes to Avoid

The post Identifying Software Requirements Using 5 Whys and 5 Hows appeared first on IT Business Edge.