Transforming 6 Business Challenges into Smart Solutions: How Intelligent Test Automation can Save You!

 Automation “executes”. Intelligent automation “thinks” and “learns”.

The tricky question of thorough testing needs to be addressed in a holistic and procedural manner. It’s good to have a great testing strategy on paper, but it’s even more important to properly execute and this is certainly not a walk in the park. Many businesses still grapple with the consequences of not implementing it.

In today’s digital age, where almost all businesses are digital, teams face intense competition to deliver innovative features swiftly and securely. With the urgency to deliver features with limited time, teams often focus on short-term product success over long-term in-depth testing benefits. This trade-off can have consequences, including software defects, security vulnerabilities, and dissatisfied users.

A proper intelligent testing automation strategy must be backed by a well-defined delivery structure, a balanced mix of technology enablers, and on-demand access to tools, technologies, test environments, and frameworks.

Critical Business Challenges

Let’s consider a banking application, and see how Intelligent test automation leverages artificial intelligence (AI) and machine learning (ML) technologies to streamline the testing process and address critical business problems.

1. Delayed Time to Market

Scenario: A banking application must update its mobile app to include a new payment feature.

Pain Point: With traditional ways of identifying defects at the last stages, there is less time to fix defects, thereby increasing the likelihood that they will be postponed until later increments or versions of the system, thus creating a “bow wave” of technical debt.

This can significantly delay product launches.

Solution: Shift Left Testing involves testing the application right from the beginning of the development process. Additionally, teams can fix defects in regression tests quickly with runtime Self-healing capability. Tools like the AI Defect Predictor tool help you to accelerate defect prediction and resolution, allowing teams to go to market with releases faster.

2. Decreased Software Quality due to Frequent Code Changes

Scenario: The development team keeps updating the application’s code due to changing requirements, security updates, defect fixes, code refactoring, or performance optimization.

Pain Point: Frequent code changes can introduce new defects. The teams struggle with a rapid development pace with minimum testing.

Solution: Intelligent Automation testing integrates into CI/CD pipelines, ensuring complete testing prior to the release. As per the scope of the build, you can scale up by getting build checks done via the 3 types of regression testing services, where the regression cycles are reduced from weeks to just hours.

• CI/CD — 15 mins to 1 hour
• Overnight with Exploratory — 8 hours
• Full with Exploratory — 24 hours

3. Lack of Testing Insights

Scenario: There is a specific defect pattern that keeps occurring in the application leading to similar issues and downtime.

Pain Point: Lack of data-driven insights impacts efficiency and leads to lower customer experience (CX). True intelligence starts when you can make intelligent decisions on demand.

Solution: AI thrives on information. It has the ability to collect and analyze data in real time. With test insights into the entire CI/CD pipeline, you can understand the effect of each change on the product, and their patterns and ultimately develop better customer experience strategies.

4. Security Vulnerabilities

Scenario: Your application collects enormous amounts of personally identifiable information and hence could be targeted by cyber criminals.

Pain Point: Misuse of data and unauthorized use of AI can have a negative impact on the business. Such malicious attacks could result in expensive lawsuits, penalties, and reputational damage.

Solution: When creating your applications, it’s critical to take end users’ privacy, transparency, and security issues into consideration. Conducting in-depth Performance Testing and Security testing ensures that the application does not have any web vulnerability that could potentially expose any sensitive data to an intruder or an attacker. Read for more click here : Transforming 6 Business Challenges into Smart Solutions

Read Next : Adhoc testing , Test Autoomation , Self healing test automation

10 Lifesaving AI Uses Every Developer/Tester Should Know

 

Software and applications have become an integral part of every industry. According to Statista Market Insights, “The world software revenue was estimated to be around $659Bn in 2023.”

As good as it sounds, this major surge in software or application requirements has made it difficult for software teams to focus on quality and speed at the same time and this is when AI comes to the rescue. From driving high-performing software solutions to interactive applications, AI plays a very important role in the software development lifecycle.

According to this report from Market Research , the Software Quality Assurance Market is expected to register a CAGR of 9.6% and USD 14.01 billion by 2025. AI is one of the contributing factors to this growth.

This article focuses on how AI is helping developers and testers alike in making the software development process more efficient.

AI Use cases for Developers and Testers

Natural Language Processing for Requirements Analysis

According to an IAG consulting study, “Companies with poor requirements definition and management spend on average $2.24 million more per project on their major projects.”

In an Agile environment, it’s a daunting task to interpret ever changing customer requirements. This is when AI comes to the rescue by processing and analyzing product requirement documents with remarkable accuracy. AI-driven NLP algorithms extract essential information and can present the analysis in desired format, helping the teams comprehend project requirements more efficiently.

Code Review Assistance

Code reviews are crucial for maintaining code quality, but they can be time-consuming, if done manually. AI technology can help with code analysis, identify code quality issues, suggest improvements, and even automate some parts of the review process. This accelerates development cycles while ensuring higher code quality.

CodeScene, one of the top code reviewing tools of 2023 leverages AI to automate the entire process and provides instant feedback as well.

Continuous Integration and Continuous Deployment (CI/CD) Optimization

Efficient CI/CD pipelines are the backbone of modern software development. AI helps with codeless testing, offers better test coverage and also comes with a reliable defect management system. Apart from that it also offers fool proof tracing to debug efficiently.

Automated Test Case Generation

Manual test Case generation for multiple scenarios can be tedious and error-prone. Additionally, test case updation can be very challenging due to changes done to reflect bug fixes and incorporate new requirements. AI-based test automation saves time, increases testing efficiency, and reduces human error.

We at Webomates use generative AI extensively to accelerate our test case creation process thereby reducing efforts significantly. Want to know more? Read our blog “Generative AI is a game changer in Software Testing”

Automated Performance Testing

Performance testing measures, validates and verifies response time, stability, scalability, speed and reliability of a system. AI can automate performance testing, simulating thousands of users and identifying performance bottlenecks. According to Dunn & Bradstreet, 59% of Fortune 500 companies have experienced 1.6 hours of downtime per week at the least. This has affected their revenue and brand reputation as well. Having AI based automated performance testing will prevent such issues and ensure smoother operation.

Security Vulnerability Scanning

Security is paramount in software development. AI can scan code for security vulnerabilities, including common issues like SQL injection or Cross-Site Scripting (XSS). Identifying and mitigating these vulnerabilities early can save both time and money.

Canva, a graphic design platform, has grown rapidly, and being a cloud company, it needed extra protection so that its tool wasn’t compromised, and this is when it deployed AI into its development process. This has helped them prevent some critical malicious attacks.

Predictive Bug Detection

AI can predict potential bugs in code before they become critical issues. By analyzing historical data and code patterns, it alerts developers to areas that may require extra attention. This proactive approach helps in delivering more reliable software.

Anomaly Detection in Production

In production environments, AI shines in detecting anomalies and issues. It monitors real-time data, identifying deviations from the norm and alerting operators when something goes wrong. This proactive monitoring minimizes downtime and enhances system reliability.

Capgemini has incorporated AI powered Anomaly Detection to identify any kind of deviations in the data logs during the development process.

Predictive Maintenance for Software

As per Webomates insights, “Software maintenance takes up 80% of the efforts and resources of an organization.” This is where self healing steps in. Self healing detects the changes in properties and automatically updates them with new attributes. This way, one can be assured that the designed test cases are functional.

Webomates took a step further and has incorporated our patented technology called AiHealing, which will address these errors without human intervention.

Automated Documentation Generation

Documentation is often an afterthought, but AI can change that. It automates the generation of documentation from code and tests, ensuring that software remains well-documented throughout its lifecycle. This benefits not only developers but also stakeholders and end-users. There are many tools available in the market for generating documentation from code like Docify, Mintlify, Docuwriter.ai and many more.

Conclusion

AI can simplify multiple tasks that are involved in the development lifecycle. Keeping up with the latest advancements is very important to ensure that you are ahead of the game.

If you are interested in transforming the way you have been testing and want to opt for a new, efficient, and faster process! Drop us a line at info@webomates.com.

How To Scale Test Automation

 

What is test automation?

Test automation, which is the key to continuous testing, is a technique to automate predefined repetitive testing tasks, using various test automation tools and testing scripts. In an increasingly agile world, test automation aids in improving feature velocity and reducing the duration of release cycles.

According to the Markets and Markets report, the global Automation testing market size is expected to grow to USD 49.9 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 19.2% during the forecast period (2021-2026).

But the question is, are the organizations equipped well to handle this growth?

How easy is it to automate the testing process, and make it scalable enough to sustain multiple changes? There are several challenges in automating the test suites. We will be listing the major roadblocks here for a quick glance. For a more detailed account, you can read our blog “Challenges in test automation”.

What is scalability in test automation?

Once you have identified and overcome the challenges to successfully automate your test suites, it is time to ensure that your automated testing process is scalable and can cater to the changing requirements.

•  Easy to write test cases
  Manual test case writing can be a tedious task and after a while, it gets boring and error-prone. Test automation addresses this problem to a certain extent, but if that too needs manual intervention/checking, then the whole purpose of automation gets defeated. To achieve scalability, test automation should be able to generate test case scripts and update them periodically.

• Ease of test execution
  The test execution process has to be simple, quick and should have a fast feedback loop. It encourages quick analysis and rectification of the issues, thus making the process scalable in case there is a major change or upgrade.

• Easy to maintain test suites
  People dread changes, especially if they lead to an extra amount of work in changing test cases to keep them in sync with the latest versions. Test automation steps in here and takes off the load. Scalability quotient depends on how easily the test scripts get updated.

• Repeatable and reliable test cases
  What is the point of having an automated test suite if it breaks every now and then? The test cases have to be reliable and can be executed multiple times making test results trustworthy.

Achieving scalable test automation

Scalable test automation is very much doable and can be achieved by following certain thumb rules, some of which are listed below.

• Automate the right tests
  Not every test can be automated. You have to identify the right tests to automate to ensure that test automation is scalable and you get the best value out of it.
  Here is a quick checklist to help you in making the decision. Automate the tests that

• Are repetitive and span multiple builds
• Require multiple input combinations
• Verify and validate critical functionalities
• Are executed in different test environments and require different hardware and software configurations
• Are prone to errors if performed manually
• Are reusable, maintainable, and resistant to changes

• Codeless test automation
  Now that you have identified the tests that can be automated and render scalability, won’t it be great if scriptwriting is minimal or even better, not required? This is where codeless test automation steps in.
  Codeless testing spurs the whole testing process by quickly generating the test cases and effortlessly addressing any modifications during testing, thus increasing the reusability and maintainability quotient of the test cases.
  
  Defect rectification and re-testing becomes much easier with codeless testing. Test engineers can conduct multiple test runs without worrying about modifying long lines of code.
  
  If you wish to read in detail about codeless test automation, then do check our article “Top 5 benefits of codeless testing”.

• Stabilize the tests before adding them to regression suites
  Let the tests stabilize before adding them to your test suite if you do not wish to spend precious resources on identifying what went wrong later. It is highly likely that the tests may fail because they were not stable and reliable.

• Self-healing test cases
  The last thing that you would want is that your application crashes because you forgot to update the test case for the minor changes in requirement or bug rectification. 
  
  Statistically speaking, approximately 20% of the test automation cost is in test script creation, and a major chunk, i.e. 80% is in test execution and test case maintenance. Clearly, it is a daunting task to keep test repositories up to date, if self-healing of test cases is not done.
  
  That is precisely why we need a test automation tool that can understand these changes and effortlessly self-heal the tests and re-execute them within the same cycle.

• Measurable KPI’s for reporting purposes
  How do measurable KPIs render scalability? They ensure that you get the right value of test automation by presenting tangible values in the reports. They also help in understanding whether the right test cases have been automated, which takes us to our very first point of this discussion.

• Better collaboration
  Better collaboration and enhanced communication aids in the quicker resolution of issues and improve transparency between the teams. This consequently leads to overall process improvement since all teams are on the same page of understanding and can work towards the common goal of how to get the best ROI from test automation.

•  Choose the right tool
  Choose the right testing tool which;

• Is intuitive and easy to use
• Is easy to integrate with an existing system
• Supports multiple platforms
• Enhances the values of test automation by leveraging the power of Intelligent test automation
• Incorporates the principles of Shift left testing 
• Is feature-rich and gives best value for your investment

We have compared a few leading testing tools which are available in the market in our article “AI test automation tools to look out for in 2022”. For additional reading, you can also go through “How to choose the right automation testing tool”.

Webomates provides intelligent test automation solutions with intelligent analytics. It leverages the power of data processing, analysis, reasoning, and machine learning to provide an end-to-end testing solution for your business.

Webomates CQ has a superior test execution and test management process with the ability to scale up or down as per the customer requirement. Here is a quick reference guide for you to make an informed decision.

If you are looking for a one-stop solution for your testing needs then look no further, reach out to us at info@webomates.com.

If you liked this blog, then please like/follow us Webomates or Aseem.

Exploring the Software Testing Trends Set to Dominate 2024

 

Understanding the Key Testing Bottlenecks

It goes without saying that modern teams need to continuously innovate, adapt, and embrace the newest trends in order to stay competitive and deliver customer-centric solutions. Despite remarkable advancements, many teams still consider that testing is a bottleneck, responsible for more delays than any other part of the application development process.

And as we enter 2024, it is critical to consider the issues that influenced the testing environment. This reflection will serve as a navigating factor for teams to understand the major bottlenecks and fulfill today’s rising expectations.

1. Limited Skilled Testing Professionals and Service

“Too little testing is done too late.”

“The developer does the testing.”

“Code reviews and testing processes take time due to lack of testers.”

These are some of the common reasons for the lack of on-time testing. With traditional ways of identifying defects at the last stages, there is less time to fix defects. This can significantly delay product launches.

2. Lack of Test Automation and maintenance

Implementing automated tests is an investment. Although many firms are implementing test automation in some form or another, very few teams have fully automated all of their QA processes. Also, the maintenance of automated tests is more expensive than that of manual tests.

It’s also very critical to understand when to automate, what to automate, and what NOT to automate to avoid automation failures.

3. Balancing Automation and Manual Testing

Striking the right balance between automated and manual testing poses a challenge for QA teams. While automation is crucial for repetitive tasks, it still lacks cognitive ability. There are aspects of testing that still require human intuition and exploration which cannot be ignored.

4. Lack of Testing Insights

Lack of testing insights lead to issues going undetected until later stages, such as user acceptance testing or even production leading to higher costs and lower customer experience (CX). Without proper insights, teams find it difficult to understand the defect reports and find solutions on-time.

5. Ensuring Ethical Use of AI in Testing

With the increased adoption of AI in testing, ethical considerations have come to the forefront. Ensuring the responsible and ethical use of AI in testing, addressing biases, and maintaining data privacy are concerns that need careful attention.

6. Increasing Complexity of Software Systems

With rapidly evolving technologies like the Internet of Things (IoT), Big Data, and Generative AI, modern systems are becoming increasingly complex, making it difficult to test comprehensively.

Testing Trends Set to Dominate 2024

The lessons learned from the challenges of 2023 set the stage for advanced technologies and new methodologies that will shape the market in 2024.

Embracing these trends will guarantee the delivery of robust, high-quality software applications in a constantly changing technical environment while also improving the effectiveness of software testing procedures.

1. Cloud-based Testing

Instead of relying on on-premises infrastructure, Cloud-based testing is a method of using cloud-based infrastructure, to run tests and test environments.

By using cloud-based infrastructure, teams can be more cost-effective and scalable as they can access a wide array of testing tools and environments, and easily scale up and down the test environments as per the changing requirements.

How can you get there?

You can move to the cloud today with Webomates — a Testing-as-a-service portal to test your application’s functionality! Its end-to-end testing framework helps you in the planning and execution of a cloud migration, along with pre-built solution blueprints, proven tools and accelerators, and dashboards for real-time monitoring.

2. AI/ML-driven Intelligent Test Automation

AI/ML algorithms will make testing smarter, with better test case generation, self-healing features, predict defects, and increased adaptation to evolving application interfaces. Predictive models can assist in deciding where, what, and when to test.

You can use AI in self-healing automation where it enables software to automatically detect and correct issues in test scripts when the UI changes, reducing the need for manual intervention and any maintenance efforts required.

How can you get there?

You can start embracing the key Intelligent Test Automation features:

• Exploratory Testing — It helps to reveal bugs that would go undiscovered during the structured phase of testing. You can perform test design and test execution at the same time.
• Efficient defect triaging — You get an in-depth analysis of automation failures to help the teams reduce their triage time. Tools like the AI Healing help you to accelerate defect prediction and resolution.
• A combination of Shift Left Testing and Self-healing capability can help you test your application right from the beginning of the development process and also fix defects in regression tests quickly.

3. Security Testing

In this digital world, cyber threats are sophisticated and teams must prioritize security to safeguard their sensitive data and protect customer’s personal data. Security testing helps you to identify weaknesses in your application’s infrastructure, configuration, or code. It can also identify common security issues such as SQL injection, cross-site scripting (XSS), and other vulnerabilities.
According to DevSecOps report, Security testing remains a serious source of frustration.

How can you get there?

Integrate testing in your CI/CD process by executing automated security tests as a part of the unit testing process. Conducting in-depth Performance Testing and Security testing ensures that the application does not have any web vulnerability that could potentially expose any sensitive data to an intruder or an attacker.

4. No-Code/Low-Code Testing

One trend that is picking up steam quickly is the shift to low-code/no-code (LCNC) platforms. Developers are using No-Code/Low-Code platforms to create, execute, and manage applications without the need for programming or scripting skills. But the complexity of testing these apps rises along with the use of low-code/no-code platforms.

This is where Low-code/no-code test automation can help where test cases can be created rapidly using visual components. With drag and drop functionalities to design test cases, it eliminates the need for manual coding.

How can you get there?

You need the power of AI/ML to generate the test scripts. Webomates CQ can seamlessly integrate into your current CI/CD pipeline and create test scripts that are compatible with various automation frameworks.

5. Mobile Testing

Today’s users rely on apps for everything — banking, retail, healthcare, entertainment — making mobile testing very important to deliver a seamless user experience across various devices, operating systems, screen sizes, and resolutions. Mobile testing involves testing on real devices along with emulators to ensure the application behaves perfectly.

Mobile testing will help you to:

• Ensure that the application’s UI is consistent across different devices and platforms.
• Verify how the application handles different levels of user activity and traffic.
• Evaluate the overall user experience and ease of navigation

How can you get there?

Webomates has integrated BrowserStack, Saucelabs, and AWS devices to help users test their app layouts and designs on real devices. It also supports multi-platform cross-browser testing along with crowdsourcing for network testing.

Conclusion

These are the emerging Software testing trends that one should watch out for in 2024

Teams need to remain aware of the frequent developments in the industry. The arrival of large language models (LLMs) and enhanced AI tools have set the stage for a revolution in testing, one where the software development bottlenecks are addressed, allowing for more streamlined and efficient processes.

At Webomates, we believe in creating a seamless integration between AI, developers and testers, bringing together the best of all worlds.