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AI Test Automation Tools to Look Out for in 2022

The need for Intelligent Testing Tools At a time when releases need to be smarter and faster, Intelligent Test Automation is one of the most influential factors in today’s go-digital and cloud-first market. And the usage of Artificial Intelligence and Machine Learning is a game-changer in the automated testing industry.

With innumerable innovative tools in the market, digital transformation is at an all time high and is exponentially evolving at warp speed. These innovative tools help you to incorporate AI into application testing across domains and industries.

Can AI tangibly provide value for businesses? Absolutely, Yes! AI is finally starting to deliver real-life business benefits. Healthcare is able to deliver better patient care. The financial sector can deliver quality products while safeguarding sensitive and confidential financial records. The defence sectors conform to compliance and the highest software quality with automated intelligent testing and analytics. The AI tools can help you automate repetitive, mundane tasks, improve efficiency across the software delivery pipeline along with the real-time decision making capability.

Stay Updated on the current Trends The Part 1 of this article covered the following current trends in AI testing that help deliver the business needs along with the delivery of flawless applications and an enhanced user experience. And to know more about these trends in detail along with our estimate of the impact of using AI in the near term, refer to the whitepaper on the software testing trends forecast and euphoria around Artificial Intelligence – Hype versus Reality.

Model Based Testing Test Insight Defect Prediction Test Data Generation Test Optimization Self Healing API Testing Exploratory Testing In this article, we list – in no particular order – the top 6 Intelligent Automation Testing providers that are leading the market by providing innovative AI and ML powered tools. Before we dive into the tools, let’s take a quick look at the parameters you need to consider for selecting a tool.

Criteria for selecting the Best Intelligent Automation tool for You While testing a specific project, a team faces many challenges in manual testing or with their current software testing tool, so there is a need for an automation tool that fulfills the team’s requirements. To evaluate a good fitness tool for your team, you need to understand your business objective, the differences in solutions offered by each tool, and what’s the best fit.

Here is a list of evaluation criteria that you can follow to choose the best automation tool –

Ease of implementation and Usability – Is the tool easy to navigate and use by the team? Scalability – Does the tool have demonstrated the capability of scaling and delivering – based on team size, the scope of testing, domain, and the application complexity level? Speed of execution – Does the tool have the capacity to self-learn and accelerate various testing tasks? Cross-platform and cross-device support – Can the tool provide support to Web, Desktop & Mobile applications for test automation? Cross Browser Testing: Can the tool support Cross browser testing as it’s a mandatory requirement in today’s digitally enabled world? Documentation and Audit-ready artifacts – Does it have a documentation process with easily exportable testing artifacts? Real-time Reports and Dashboards – Can you get instant reporting and analysis to help reduce the feedback loop between developers and testers? Pricing – Pricing is also an essential factor to be considered. The Top 6 Intelligent Automation Tools for You! Although AI technologies have advanced significantly in recent years, very few teams have adopted them. Based on research, we have compiled a list of innovative AI Testing Tools to look out for going into 2022 that will surely help you.

1. Tricentis Tosca Tricentis Tosca is a software testing tool that is used to automate end-to-end testing for software applications. It combines multiple aspects of software testing (test case design, test automation, test data design and generation, and analytics) to test GUIs and APIs from a business perspective. Its codeless, AI-powered approach accelerates innovation across your enterprise by taking the bottlenecks out of testing and the risks out of software releases.

Tricentis Tosca features technologies for:

GUI testing API testing Mobile testing Service virtualization Test data design and generation Business intelligence and data warehouse testing Exploratory testing Link – https://www.tricentis.com

1. Worksoft It provides an automation platform for test automation, business process discovery, and documentation supporting enterprise applications, including packaged and web apps. It provides an integrated test data management tool.

Main features of Worksoft are:

Cross-platform support Cross-browser support Real Device Testing Dashboards, Reporting and Analytics API Testing Functional Testing Link – https://www.worksoft.com

1. Webomates CQ One of the most prominent and promising trends going in 2022 will be the ability to predict defects with Artificial Intelligence. That’s where Webomates’ patented AI Defect Predictor tool comes in! You can know your defect in 20 seconds!

Along with self-healing capabilities using the new-age intelligent technologies, Webomates infuses intelligence into systems and applications across the software development lifecycle. It adopts automation solutions to support developers to operate with quality and agility.

They have a suite of 14 AI tools for you! These tools are used in the entire life cycle right from test case estimation, test case generation and test model generation to reduce the overall set up time from months to weeks.

Promising features of Webomates include:

On demand Regression Testing A self-service SaaS portal to test your application’s functionality Test management suite API Testing GUI Testing AI Defect Predictor to detect defects Test case Healing Real-time monitoring portal Exploratory testing Link – https://www.webomates.com

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If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

DevOps: Continuous Testing

DevOps demands a high level of coordination within various capacities of the deliverable chain. It also means that the boundaries between different roles of contributors in the chain become fluid. DevOps encourages everyone to contribute to the chain. So, amongst other things, a dev can configure deployments.

Challenges In today’s digital age, developers must move faster and smarter and are held back from continued maintenance and continued innovation. DevOps posts a challenge to the entire business in the way it thinks of building and launching a product. With amplified visibility of defects and their impact on our day-to-day lives, it has become evident that software testing plays a pivotal role.

Continuous Quality = CI+CD+CT Webomates CQ makes adding system tests to the CI/CD tool chain effortless. The platform can be invoked via an API and the results are posted back into your CI/CD system. Read for More : DevOps: Continuous Testing

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

Will AI Completely Eliminate Human Involvement In Testing

The rise of the machines in the testing arena

Artificial Intelligence started in the 1950s, picked up pace steadily, braved the AI winters, and now, it is omnipresent in different fields like defense, medicine, engineering, software development, data analytics, etc.

A survey was conducted for the World quality report, about how the organizations plan to utilize AI in their QA activities. The results were recorded in the World quality report 2020-2021 and are represented in the following chart.

AI in QA activities

Approximately 84% of respondents had AI as a part of their growth plan. The rest of the survey portrayed a positive picture for AI usage in software testing. It is quite evident from the above survey that artificial intelligence holds the key to an industrial revolution, with more and more organizations leaning towards using AI in various operations. This has opened new avenues for software testing to ride the AI wave and accelerate the CI/CD/CT pipeline with guaranteed high-quality results.

The following figure gives a quick overview of how AI is used to improvise software testing

Improve software testing

Learning: It involves understanding the testing process, codebase, underlying algorithms, data bank, etc. to fully equip the AI tool with the knowledge to apply in the testing.

Application: Once the AI tool is equipped with the knowledge, it can then apply its learnings for test generation, execution, maintenance, and test result analysis.

Continuous improvement: It is the key to AI enhancement. As the AI tool usage grows, so does the data and scenarios at its disposal from which AI can learn and evolve, and consequently apply its knowledge to further improve the testing process.

In nutshell, AI application in its current state equips itself for predictions and decision making based on the learnings from a set of predefined algorithms and available data. Thereby, it aids in improvising automated testing tools by speeding up the entire testing process with precision.

But can AI completely take over software testing, thus eliminating any kind of human involvement?

The following figure gives a quick overview of where the balance is tipped in AI’s favor and where the humans have an upper hand.

Man vs Machine

Where AI wins

Test case generation: Test case generation with AI saves a significant amount of time and effort. It also renders scalability to software testing. Test data generation: AI can generate a large volume of test data based on the past trends within a matter of seconds, which otherwise can take more time if left for manual work. Test case maintenance: AI can dynamically understand the changes made to the application and modify the testing scope accordingly. Predictive analysis: AI certainly has an advantage when it comes to analyzing a huge amount of test results in a short time. It can scan, analyze and share the results along with the recommended course of action with precision. We have a detailed blog that covers the benefits of AI testing and intelligent automation. Click here to read more.

Where humans are still needed

Edge test cases: There might be certain test scenarios where a judgment call needs to be taken. If AI does not have enough data and learnings from the past, it may falter. That is when human intervention is critical. Complex unit test cases: Unit testing for complex business logic can be tricky. AI can simply generate a unit test case based on the code it has been fed. It cannot understand the intended functionality of the module. So if there is a flaw in the programming logic then the unit test may produce an undesired result. This is when the developers have to step in. Usability testing: AI can test any system “mechanically”, but the end-user takes the final call when it comes to addressing the usability of the software. AI, in general, faces certain roadblocks in its software testing journey. We have elaborated on those challenges in another blog: “Challenges in AI testing”. Read it to have a deeper insight on the subject.

Best of both worlds – AI and Human brilliance with Webomates

Let us go back and refer to the survey mentioned earlier in this blog. While a large % of respondents are still contemplating the usage of AI in various parts of the testing process, we have already made several breakthroughs with 14 AI engines incorporated in our platform Webomates CQ.

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If this has piqued your interest and you want to know more, then please click here and schedule a demo, or reach out to us at info@webomates.com.

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Top 6 Ways to Boost Mobile App Testing

Challenges are part and parcel of our daily lives. Eventually, what matters most is the way we deal with them. There are a plethora of challenges when it comes to mobile app testing. But what differentiates better companies with the mediocre ones is how they tackle these challenges. To stand out, you need a smart approach to test your mobile app, that finds defects early and supports the rapid development cycle in an Agile culture. This article emphasizes the top six ways to boost mobile app testing processes which are discussed below.

boost mobile app testing Device Fragmentation An application can be used on a variety of devices with varying screen sizes. The target could be anything between a 4-inch phone to a 10-inch tablet. Though mobile device fragmentation is not as much of an issue with iOS devices, Android, in particular, has thousands of different carrier settings, form factors, and operating system variations that must be accounted for during testing. According to the latest report by ScientiaMobile’s WURFL, there are more than 63,000 device profiles and this has grown at almost 20% per year.

Device Fragmentation A vast array of screen sizes requires the application to be tested in all the possible screen sizes and aspect ratios. To overcome this challenge, it makes sense to test the application with as many real-life devices as possible, to better understand how the app looks like or behaves on different screens.

Refer to this Device Metrics guide to calculate the right measurements for design across devices.

You can rely on the emulators during the initial stages of the app development, but as you go deep, real devices should be brought into the game. A better approach would be to use device farms or real cloud devices as they can help minimize the number of devices you need to test in-house and reduce the time to market (TTM) significantly. Device farm provides an ideal platform to the testers to evaluate an application’s operability manually from an end-user perspective. BrowserStack, Saucelabs, and AWS provide device farms that allow users to test their app layouts and designs across more than 2000 device-browser combinations on real devices.

Overcome Potential Storage Issues Storage space is one of the key swinging factors in mobile devices. Applications behave unpredictably if no disk space is available, which makes it pivotal to contemplate the amount of data an app will download to the user’s devices.

This can be deciphered by minimizing the storage requirements that the program places on mobile devices. It is crucial to check the memory requirement for downloading, installing, and running the application and verifying that a user with a limited data plan can download the application.

Test Different Network Settings Test Different Network Settings Unlike desktop devices, mobile and tablets generally hop around different wireless networks (3G, 4G, 5G, LTE, Wi-Fi). Despite the considerable progress made in Wi-Fi technology, in particular, since the 1990s, wireless networks are slower and less reliable as compared to the wired equivalents. As various network carriers offer different connectivity options to their users, fragmentation, in this case, is far more complicated than OSs. Testers should take into account how their application’s performance differs from one carrier to another. App performance testing should not exclusively be conducted on Wi-Fi. Instead, an application should be tested on actual 3G, 4G, 5G, and LTE mobile communication standards. Additionally, the app’s behavior should be tested when there is no connectivity or during a sudden shift in the networks, for example, from 4G to 3G. This volatility of mobile networks can be a challenge, especially for the applications having access to VoIP and video conferencing. As the latency and bandwidth of various networks can vary, keeping the differences in mind allows a QA specialist to not only optimize the app’s performance but also to improve user experience.

Performance Testing Call to action using social networks – Concept with Fibonacci sequence “It’s better late than never.” At times when the development teams face time constraints, they are tempted to skip mobile app performance testing, which leads to catastrophic results. Performance testing is one of the key attributes in determining whether an app will be successful or not. It is crucial to measure the performance (speed, reliability, and competence) of an application under particular workloads.

Validating whether an application will be able to withstand high workload and stress will help determine when the app’s performance is compromised and which actions should be performed to redirect the risks. Read for more : Mobile app testing

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

Overlapped Regression for Agile teams

Webomates’ CQ is composed of three different services:

A full regression using both Exploratory and Test-case based testing Overnight – which is a guaranteed execution based solely on a test case Smoke tests that are pure automation executions Full Regression

The full regression is a very thorough execution and always increases the envelope of quality. A depiction of a full regression, covering a product composed of 4 modules, is shown below. A full regression is composed of two different parts:

Test case based. The blue bars show the area of the product-module that is being covered by documented test cases. This is a baseline of guaranteed capabilities that are validated by extremely well-defined test cases with concrete execution steps and multiple validation points. Exploratory based. The orange dots show defects that are found in a particular regression run. Exploratory testing is typically either charter based (a charter is a guidance for what area to test) or completely random. Execution is time based and the intent is to explore the software as much as possible and discover defects.

However, it is HEAVY in terms of both time (24 hours) and in terms of effort/cost.

Overnight Regression

In an Overnight Regression, solely test-case based regression is carried out on only a single module.

With Webomates’ execution guarantee ALL module test cases will be executed, including modified test cases. This is important to note as test cases that change due to a defect fix or feature change will also be validated! Thus, an agile development team working on a particular module, on a specific day, can do a check-in in the evening and come back the following morning confident that all the baseline test cases for the module are working! By the morning, if any defects are discovered, they are available and ready for the development team to review. And the same can be done for every other module.

Smoke Tests

Smoke tests are a subset of the test cases that are completely automated and can be run at any point in time by the customer him/herself. Smoke tests are structured to be end-to-end as well as module focused. The main advantage is that they can be invoked at any point in time and can be completed within 15 minutes. However, there is no exploratory testing included, there is no execution guarantee and no defects are created. The development team needs to look at the Pass/Fail report to determine the result of the regression run. Agile teams use a smoke test multiple times throughout the day to assess the state of the build prior to invoking an Overnight test on the module under development.

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If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

Defect Triaging: The catalyst in Bug resolution process

What is Defect Triage? Defect triage is a process to prioritize the defects based on severity, risk, frequency of occurrence.

Though triaging can be implemented on any sized project, its benefits are more evident and tangible in large-sized projects. The list of defects could be proportionately big for large-sized projects. Alternatively, in a highly agile project that is spinning up, triage can help focus the team on the “right” defects. Either way, the triage process identifies defects that need immediate attention, while some may be deferred.

The triaging mechanism helps in preparing a process for testers and developers to fix as many as possible defects by prioritizing them based on parameters identified and fixed by the team.

Ideally, every test cycle should have regular triage sessions. Frequency can however depend on the number of defects identified with every test.

Why do you need Defect Triaging? Every time the defects are reported by the testing team, there are always certain if’s and but’s involved. The developer(s) need to understand “what” defect and “when” was that defect discovered, so they can work on the “why” part of it.

If defects are not recorded, mapped, and reported properly, then the time and efforts involved in identifying the root cause and rectifying them are much higher.

It is worthwhile to note that multiple defects are reported at a time and it is vital to identify which one to fix first based on business and functional needs.

Defect triaging helps the development team to fix the bugs based on their priority and severity. Since all the relevant information about the defect is readily available to them, it makes the fixing process easier and less time-consuming. If triaging is done correctly, then it significantly reduces the time taken between reporting the defect and its resolution.

Thumb rules of Triaging Rules All reported defects have been reviewed All accepted defects have been prioritized All accepted defects have severity attached All rejected defects should have plausible explanations for the testing team Every defect has been assigned to an appropriate owner, individual or team Root cause analysis for every accepted defect has been done How does Defect Triaging Work? Defect Triaging Process. The defect triage process involves holding a session with a triage team, which includes stakeholders like Product Manager, Testing Manager/Lead, Development Manager/Lead, and Business Analysts. The goal of this team is to evaluate the defects, assess them, and attach priorities and severity level.

Priorities correspond to business perspective and severity corresponds to technicalities.

Many times, few defects may be considered trivial and rejected at this stage. Accepted defects are prioritized and assigned for resolution.

Factors to be considered while evaluating and prioritizing the defects are:

The validity of the defect Time sensitivity for resolution The complexity involved in the resolution Business impact This process is not just about attaching severity and priority to the defects. It also provides all relevant information required to track, replicate, and fix them.

The invalid defects and the basis of their rejection are also recorded for reference purposes.

Root cause analysis for every single defect is conducted. This analysis forms the basis for an improvement plan which ensures that chances of getting a similar defect are significantly reduced.

The outcome of the triaging process A report is generated based on the outcomes of the triaging process. A typical defect triage report will have the following information:

DEFECT TRIAGE checklist Challenges in Defect Triaging Challenges in Defect Triaging Every process tends to hit roadblocks if not planned and executed properly. The following challenges are commonly seen while conducting defect triage.

Lack of proper communication between the development team, testing team, product owner, and business stakeholders Lack of standard defect tracking system Improper assignment of priorities and severities Improper assignment of defect owner How Webomates makes Defect Triaging easy? Webomates has a comprehensive defect triage mechanism. We ensure that the stakeholders get the correct and right amount of information to resolve issues with optimal usage of resources and time.

We have defect triage meetings on a weekly basis, monthly basis, and often with every Sprint. In order to help our customers in optimizing time for review defects, our reports include all triaged defect data. The sample report of the Webomates triaging process is shown below. Read for more : Defect triage

If you are interested in learning more about Webomates’ CQ service please click here and schedule a demo or reach out to us at info@webomates.com.

Software Testing

What is software testing? Software testing is a procedure, in which every one of the stages like test planning, test development, test execution, result investigation, tracking of bug and reporting are accomplished effectively. Validation and verification of Software is software testing in software engineering. Testing is a critical segment in the software development life cycle particularly with bugs and imperfections being boundless and phenomenal. Software Testing is a continuous procedure and it ought to be conducted all through the development to guarantee that the application functions as per the expectations. Software Testing as a Process Software testing is a comprehensive procedure that incorporates connected processes. Three things you check through these procedures: Software completeness in regards to functional necessities; discovering errors that are technical in nature and guaranteeing the software is without bug; evaluating ease of use, execution, security, localization, installation and compatibility. The software can go overall, in segments, or within a live framework. However, if a product needs to be useful, it needs to go through every one of the tests. After every step of testing, the software goes for amendments. Subsequent to settling these mistakes, the software testing team again runs the following series of tests. This cycle proceeds until the point where the software reaches the desired level of quality. Role of Metrics Reporting in Software Testing Metrics in software testing can be characterized as standards of measurement. Software metrics are utilized to quantify the nature and quality of the project. In simple words, metrics is a unit utilized for describing an attribute. Thus, the metric is a scale for measurement. Some example of Test Metrics Test Execution summary — Pass vs Fail vs Blocked % Defects per module Test Coverage percentage Defects by Priority Requirement to test case traceability What is Software Test Measurement? Software testing measurement is the quantitative sign of degree, amount, capacity, dimension or size of some attribute of a certain process or product. Let’s take a brief look into what software testing is all about with the assistance of 5 Ws (Who, What, When, Where and Why) and 1 H (How). 5Ws and 1H explained through a project management-based scenario. Taking a Software testing in software development project, for instance, we consider a situation where development team has discovered that technology they are utilizing isn’t completely perfect with the current frameworks of their client, as was already thought. So the task team can consider utilizing 5Ws 1H (or 2H) to comprehend the problem and its degree of impact. Application of the first W — ‘’What” What do the users do? What are the objectives they have to achieve? You have to understand their tasks all in all and also the assignments that relate to the software testing or framework that you’re planning. Usefulness Vs Performance Vs Load Vs Security Testing (What) An imperative assignment for testers is to confirm if the application complies with its determinations and necessities. Utilizing either Manual or Automated software testing philosophies, testers would need to test the applications UI, the APIs, the systems networking to guarantee that the application functions in the manner in which it is required to. Testers do not just need to guarantee if the application can deal with a lot of information they also need to validate that numerous users are going to access the system. Software performance testing is done to benchmark the application performance in certifiable conditions and to recognize the bottlenecks that can block performance. In this regard, the testing team can make the following questions to create an understanding of the crucial problem and extent of the problem. What is the technology we are utilizing for software testing development? What technologies were considered at first for this development project? What was known about the current system(s) of the client? What checks and confirmations were done to affirm the similarity of the technology being manufactured and the arrangement compatibility of new technology on existing client system(s)? What (assuming any) affirmed or unapproved adjustments/changes have been made after dialogs on technology similarity since the beginning of the task? What expertise is accessible to the team to enable them to comprehend the similarity problem? What are the acknowledged procedures for overseeing similarity and technological collaborations? What rules or standard operating procedures (SOPs) are accessible to manage such problems? What actions were taken once the problem was detected during testing? Application of the second W ‘Why’ The task team may ask ‘Why’ questions to get a more granular comprehension of the problem and look to clarify triggers or drivers that may have added to the problem. A portion of the questions for software testing that can be asked are: Why has it happened that two technologies are currently observed to be inconsistent? Why was this problem not detected before or toward the beginning of the software testing or project? Why technologies can’t be made good? Why were quality affirmation forms not ready to recognize the problem? Why were project teams or specialists engaged with the task not ready to identify the problem? Application of the third W “When” Test-First or Test-Last (When) Software testing can either be composed before the code is finished or after the code is finished. Test-Last help in checking that the code fills in obviously while Test-First recognizes how the code should function. Composing the test initially has its very own favorable circumstances despite the fact that it might appear to be variant. It keeps imperfections and bugs from entering the code and later aides in outlining. Test-Last help in checking that the code fills in of course while Test-First recognizes how the code should function. Composing the test initially has its own points of interest in spite of the fact that it might appear to be atypical. It keeps imperfections and bugs from entering the code and later aides in outlining. By utilizing ‘When’ questions, project teams can time stamp the events and comprehend the connections among different events that may have affected the rise of the incompatibility problem. When the problem or need for software testing was originally identified? When was client system(s) information/architecture assessed? When were similarities problems mapped and examined? When any similarity tests were done and similarity was discovered satisfactory? When was any incongruence affect appraisal done? When was the problem taken up with client or client educated? Application of the fourth W “Who” Who are the users? What are their characteristics? What learning and experience do they convey to their tasks? Are there any other groups of users? Assuming this is the case, what separates them from one another, and which client bunches are generally vital? The subject of whose desires to meet dependably emerge. Would it be those of the Developers or of the Users? Users and engineers have their very own assumptions regarding the application and the codes. The desires from the two sides during software testing ought to be weighted legitimately. The project team can make questions to distinguish the people involved in contributing to the particular problem. A few questions that might be inquired: Who is in charge of guaranteeing technological similarity within the project team? Who is in charge of data gathering and client system(s) mapping on task team side? Who is in charge of plan and change administration endorsements? Who is in charge of giving client system(s) data and specialized points of interest to the task team from the client side? Who is in charge of regulating the similarity of software testing and detailing?

Who is in charge of problem identification and acceleration? Who is in charge of problem firefighting and contact? Who detected the problem or came to know the problem first and who were educated first?

Who heightened the problem and educated the concerned parties? Application of the fifth W “Where” By asking ‘Where’ questions, the project team can improve the handle of the source(s) of the problem. A few questions that can be inquired during this stage of software testing are:

Where is the client system(s) found? Where is development teams found? Where is quality confirmation for software testing found? Where is documentation on framework similarity kept?

Where are any endorsement/change administration documentation on framework engineering and similarity kept?

Now Application of the H “How”

In the metric reporting of 5W and 1H, anything that disappoints the client is a defect, thus understanding the client and client prerequisites is the most critical problem in building up a metric reporting software testing culture.

This is a critical thinking administration philosophy that can be connected to a business procedure to recognize and take out the main drivers of defects within software development, eventually enhancing the key software features and sparing expense for the association. In such a manner, the fundamental objective of metrics reporting is that any software development in an association should be monetarily suitable.

5W and 1H metric reporting is a management logic, enables an association to apply a restrained, information driven methodology that consistently acquires development process execution by lessening the inconstancy in every software design.

This makes a culture in an association went for figuring out how to fabricate forms that convey the business yield with immaculate quality. Metrics reporting additionally centers on estimating and controlling the variety at each phase of software testing and its development.

To comprehend the sequence of differences between related events, the project team can ask how to addresses the 5W’s, for example – How did the problem occur?

How has the succession of occasions prompted the discovery of the problem?

How are similarities problems dealt with and enter exercises detected in the task?

How are similarities documentations arranged, shared and put away? How is similarity testing performed?

How the capability of inconsistencies limited and superior comprehension is assembled about mechanical similarity? How are jobs and duties inside task portrayed and how is responsibility guaranteed?

How is detach between project teams limited and legitimate correspondence kept up?

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Uncovering the Meaning of Exploratory Testing

Read for more : Software Testing

Our CQ portal provides metrics reporting which benefits the software testing of your software build. If you are interested in learning more about Webomates’ CQ metrics reporting please click on the link and schedule a demo.

Benefits of Intelligent Automationation

Artificial Intelligence is a technique that enables a computer system to exhibit cognitive abilities and emulate human behavior based on pattern recognition, analysis, and learning derived from available data with the aid of predetermined rules and algorithms.

Machine learning and deep learning are two terms that are often used every time Artificial intelligence is discussed. People tend to use these interchangeably, however, there is a fundamental difference between them.

Understanding the fundamental difference between AI, ML, and DL fundamental difference between AI, ML, and DL Artificial intelligence is the superset of machine learning and deep learning.

Machine learning is a subset of AI which aids computer systems in learning and decision making without explicit human intervention. It works on pattern recognition technology and works with predefined algorithms to understand, learn, process, infer and predict, based on past data and new information. Its prime focus is to aid in decision-making. AI improves as ML improves.

Deep Learning is a subset of machine learning, also called scalable machine learning. It helps machine learning algorithms by extracting zeta bytes of unstructured and unprocessed data from data sets.

What makes intelligent automation important in software testing Test automation promised to revolutionize the world of testing when it was first perceived and implemented. It delivered on that promise by improving overall testing speed and results. However, as technologies and processes further evolved, there was a need for improving the testing process too.

If you want to understand the journey of the testing process from manual to AI era, then read our blog “Evolution of software testing”.

Automation eased the testing load, but it could not “think”. For instance, test automation can execute thousands of test cases and provide test results, but human intervention is needed when it comes to deciding which tests to run. Adding the dimension of intelligence can add analysis and decision-making capability to test automation.

Intelligent automation works on data like test results, testing metrics, test coverage analysis, etc., which can be extracted and utilized by AI / ML algorithms to identify and implement an improved test strategy for efficient testing.

As per the Gartner study, “By 2022, 40% of application development (AD) projects will use AI-enabled test set optimizers that build, maintain, run and optimize test assets”

Let us explore further how intelligently automating the testing process helps in improving overall QA operations.

test reliability with improved accuracy Higher level of test reliability with improved accuracy In the era of DevOps with frequent and shorter development cycles, continuous testing is conducted for every minor/major change or a new feature. While test automation has helped a lot in reducing the testing burden, adding AI to automation can enhance the overall testing process, since it keeps evolving based on new information and analysis of past data. It also aids the teams in identifying the tests for better test coverage. With intelligent automation tools doing a large portion of recurring tedious tasks, the developers and testers can focus on other aspects like exploratory testing and finding better automation solutions. Improved risk profiling and mitigation with enhanced test result analysis Intelligent automation renders the ability of risk profiling to testing. Intelligent automation and analytics help the testing and development teams to have a better insight into the impact of code changes and risks associated with those changes. Appropriate actions can be taken based on these insights and issues can be intercepted much earlier Deeper insights in test results and predictive analysis Test reports and analysis are critical processes of software testing. It helps the teams in understanding the loopholes in their current test strategy and consequently aids them to define better strategies for the next test cycle. AI-infused tools can analyze and understand the test results, spot the flaws and suggest the workarounds. These tools constantly learn and update their knowledge base with every test cycle, based on test result analysis and apply that knowledge to improve software testing by detecting even minor changes and predicting the test outcome. Improved defect traceability and prediction is a game-changer when it comes to optimizing the test strategies. Boosts efficiency by transforming DevOps with benefits of AI Ops and QA Ops To match pace with dynamic software testing demands, DevOps has to be augmented with the power of artificial intelligence. QA Ops have gained importance in the past few years and enabling it further by using intelligent automation will ensure faster time to market with better quality. Faster delivery with improved results Intelligent automation plays an important role in accelerating releases since it optimizes the whole testing process based on a comprehensive analysis of previous test results. Continuous testing for frequent changes can be time-consuming, but AI/ML expedites the whole process by identifying the right set of tests to be executed, thus saving a significant amount of time and resources. Read More about : Intelligent process automation

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Selenium Automation won’t get you to DevOps

Manual Testing is evergreen. However, the zest to expedite the testing process and reduce human intervention paved the way to Automation testing 20 years back and AI Automation is now becoming the need of the hour. Who doesn’t love continuous delivery?

With an aim to achieve collaboration across teams, integrate customer feedback on the go, and target small but incremental rapid releases, more and more teams are going the Agile DevOps way. Continuous delivery is the ultimate goal, and Continuous testing is the way to achieve it.

The 11th edition of the World Quality Report decodes the role of software quality in assuring business growth and outcomes. As per the report, test automation is the biggest bottleneck to deliver “Quality at Speed,” as it is an enabler of successful Agile and DevOps adoption.

Contribution to business growth and outcomes rated most important QA priority by IT and business

Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with the latest build.

It requires automated processes to achieve agility to build, test, release and deploy the software. Each build goes through Integration testing on the build server, Functional testing and regression testing on the test server, Deployment testing on the staging server. If the build passes, it advances to the next step. If it fails, it’s time to stop and fix the issue.

How do you choose an automation tool for your team?

There are a plethora of open-source and commercial automation tools to choose from. You have IBM Rational Functional Tester, Test Project, Accelq to name a few.

With such a broad range of tools available, organizations can find it daunting to choose the best one that conforms to their project requirements. However, Selenium is the most preferred tool. As per the HG Insights report, around 62,698 companies use Selenium.

What is Selenium

Selenium is a free open source suite for automated testing for web applications across different browsers and platforms. Selenium focuses on automating web-based applications. You can use multiple programming languages like Java, C#, Python, etc to create Selenium Test Scripts. Selenium Software is not just a single tool but a suite of software, each piece catering to different testing needs of an organization.

Selenium is considered among the top tools for browser-based and cross-platform regression testing due to the following factors:

It’s an Open source framework

Provides Multi-Browser Support

Multi-Language and Multiplatform support capability Parallel and fast test cases execution

Read for more : Selenium Automation

Webomates has integrated solution Webomates CQ which helps companies to test the Mobile app properly and with effectiveness

Enhancing Test Automation with AI

In an increasingly competitive market, there is a rise in demand to release software faster to meet the customer requirements, without any compromise in the end product’s quality.

This puts an additional load on the organizations to develop and test faster for quick releases. Continuous testing is an end-to-end testing process that speeds up the CI/CD pipeline, by incorporating automated processes and tools for testing early and testing often at all points of time. Test automation is an integral part of Continuous testing.

Test automation is a technique to automate predefined repetitive testing tasks, using various test automation tools and testing scripts.

Test automation has marked benefits in terms of accuracy, scalability, dependability, enhanced test coverage, time and effort saving. But is it enough? Test automation eased the testing load, but it could not “think”. Augmenting test automation with the capabilities of AI introduced the dimensions of continuous learning, analysis, and decision making to the continuous testing process by emulating human behavior without any actual human involvement.

As per the recent study conducted by Gartner Inc., the business value of AI will reach $5.1 billion by 2025. In another study conducted by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030.

Let us explore how embracing AI test automation improves the QA Ops process.

How AI enhances test automation AI test automation, aka intelligent test automation, can spot anomalies, learn from patterns, analyze the data, and then if required, can update the test scripts to reflect the intended changes. This section explains how it does all this and takes testing to the next level.

test automation Testing basics Getting the basics right is a good start for the testing process. Test data generation and test case generation is an important task and needs to be done with utmost care.

Understanding, analyzing, and then translating the requirements to test cases is a time-consuming job. AI-based tools can do it for you in less time and you can redirect your effort for other tasks.

With continuous testing, the amount of data generated, aggregated over multiple cycles, is huge. Sifting through that data, analyzing the patterns and trends to act as feedback for the next cycle is a herculean task. Also, the input data and test cases need to be updated with every cycle and have to be in sync with the requirements. Using AI/ML shares the load and does the maximum job by generating test data and test cases by learning from previous data/reports and incorporating the new requirements.

Test execution Test smart – It is not feasible to execute the whole test suite for the smallest of the changes made in the application under test. A smart strategy would be to identify the test cases that are directly impacted by the change and execute them. But with Continuous integration and testing, it becomes increasingly challenging to do that. Intelligent test automation comes to the rescue by analyzing the data from previous test cycles and identifying the right test cases to be executed for the changes done. This saves significant time and effort.

Test right – False failures are the bane of test automation. These are the scenarios when the test automation tool ends up marking a true pass case as a failure. Do take out some time to read our blog “Test automation challenges – False failures” to understand this better.

False failures can lead to unnecessary delays in the schedule because every failed test case needs to be triaged and based on its priority needs to be addressed accordingly. The issue of false failures can be addressed by applying AI/ML algorithms to test automation. The analytical capabilities of AI ensure that the test cases are marked correctly as true pass or true fail by learning from patterns of test results of previous test cycles and new information for the current cycle. Read for more : Test Automation with AI

Webomates has integrated solution Webomates CQ which helps companies to test the Mobile app properly and with effectiveness