How To Choose The Right Automation Testing Tool

 Are you exploring the options for automating your testing process?

Are you worried about the ROI of test automation?

Will you be able to convince your decision-makers in investing in an automated testing tool?

What if we give you a ready reckoner guide that can help in making the right decision?

Interested? Go on, read more….

Automating the testing process aids the organizations in accelerating their development and delivery cycles significantly with a higher level of quality of the end product.

Let us take a quick look at the major benefits of test automation and then we can move on to what all you need to consider while looking for an automated testing tool that fits perfectly for your needs.

Now that you have seen that it is worth spending time, effort, and money in getting a test automation tool, let us now discuss what key points you should consider while making the crucial decision.

Key Point	Details
Kind of tests to automate	Tests that can be automated should beHighly repetitiveVery frequently executedStableWith predictable outcomesLook for the tool that covers your requirement and offers scalability in terms of the number of tests to execute so that tomorrow if the number of tests increases/decreases, the test suite can be adjusted accordingly.
Skills of the team	What is the learning curve for using the tool?Does it need extra skill to use the tool?Do you have SME on your team? If not, then does the vendor extend support and expertise?
Ease of integration	Is it easy to integrate the tool with your current CI/CD pipeline without disturbing the current workflow?Does it require an extra infrastructure setup?
Ease of use	Does the tool have an option of codeless testing?Or does your team have the capability to write the test scripts?How intuitive is it to use the tool?Is it easy to follow the workflow of the tool?
Versatility	Is the testing tool versatile enough to conduct different types of testing? Integration testing, functional testing, performance testing, UI testing are just to name a few.;Is it compatible with different platforms/browsers/OS?
Smart test management	Test management includes generating the test cases, executing them, and maintaining them. Consider the following points while analyzing the test management capabilities of the tool.Time taken to generate the test casesEfforts involved in generating the test casesAre the test cases generated by the tool reusableTime taken to execute a test suiteDoes the tool have an option to modify test cases in case of any changes?Does it execute the modified test case in the same cycle?
Comprehensive reporting	The management needs to see the results and numbers as an assurance that their investment was worthwhile. A testing tool with a good reporting feature that generates easy to understand reports gets bonus points while shortlisting.The reporting feature should haveDetailed test analysisReport only true failures and filter out false failuresOption for real-time alerts for defects
Budget	Keep in mind the budget allocated for test automation. Check for various costs likeInfrastructure setup costsCosts associated with upgrading skill setLicensing costs for any third-party tools, if required.Many tools offer various plans based onNumber of tests to be executedTime quantumUse of their infrastructureAll these factors have to be accounted for towards the total value of investment towards automation.
Trial package	Does the tool have a free trial run option? A trial run helps you to get a feel of tool functionalities and aids in making an informed decision.
Technical support and assistance	Does the vendor provide training to your team?Do they have a detailed technical manual for using the tool?What kind of process does the vendor have for lending technical support?Is it online, email, telephonic, or in-person?Is it 24X7 support?What is the turnaround time for addressing the issues?

The Webomates advantage

There are multiple options available in the market ranging from open source tools to customized ones. It is up to you to make the wise choice keeping in mind the long-term ROI.

As mentioned earlier, test automation has marked benefits in terms of accuracy, scalability, dependability, enhanced test coverage, time, and effort saving, but it cannot “think”. Enters, AI test automation, aka intelligent test automation.

AI 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. You can read more about the differences between test automation and AI test automation on our blog by clicking here.

What if we give you a tool that meets all your testing requirements, fits in your budget, and is intelligent enough to help you in analysis and decision making?Webomates CQ is a revolutionary AI-based testing tool that meets all the criteria mentioned in the previous section with the service level guarantees to support its claims. Read for more : Application 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.

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.

1. Model Based Testing
2. Test Insight 
3. Defect Prediction
4. Test Data Generation 
5. Test Optimization
6. Self Healing
7. API Testing
8. 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 –

1. Ease of implementation and Usability –  Is the tool easy to navigate and use by the team?
2. 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?
3. Speed of execution – Does the tool have the capacity to self-learn and accelerate various testing tasks?
4. Cross-platform and cross-device support – Can the tool provide support to Web, Desktop & Mobile applications for test automation?
5. Cross Browser Testing: Can the tool support Cross browser testing as it’s a mandatory requirement in today’s digitally enabled world?
6. Documentation and Audit-ready artifacts – Does it have a documentation process with easily exportable testing artifacts?
7. Real-time Reports and Dashboards – Can you get instant reporting and analysis to help reduce the feedback loop between developers and testers?
8. 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

2. 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

3. 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

Read for more click here

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.

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

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.

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.

Read for more click here

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.

If you like this blog series please like/follow us Webomates or Aseem.

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.

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.

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

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

“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:

1. 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.
2. 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.

Read for more : click here

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

• 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?

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:

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 triaging

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.