How can AI help testers? – A Moolya perspective

Following the announcement of the Generative Artificial Intelligence (AI) software called chatGPT the conversations surrounding AI and Machine Learning (ML) have taken a serious turn. 

After years of being a nascent topic now AI has become a central point for all tech conversations. AI suddenly has become this messiah, driving innovation and transforming how we approach problems. 

In the world of software testing too, AI is revolutionizing the way testing is conducted, enhancing efficiency and accuracy while reducing human effort. 

Let us take a closer look at how AI could be applied practically in software testing and the benefits it can bring in. We will also look at some of the skills needed to integrate AI into testing. We will also understand a couple of real-world examples that demonstrate the technology’s impact on the field.

There are numerous benefits of AI in software testing, helping enhance the efficiency and effectiveness of the testing process. Listed below are a few specific applications of AI in software testing and their  benefits:

 1.  AI-powered test automation tools:

Tools like Testim, Appvance, and Mabl leverage AI and machine learning algorithms to automate test creation, execution, and maintenance. We see reduced human intervention, faster cycle times and greater test coverage as the most tangible benefits. 


    • Reduced human intervention: AI-powered test automation tools can handle repetitive tasks, freeing up testers to focus on more complex and strategic testing activities.
    • Faster cycle times: AI can execute test cases more quickly than manual testing, enabling faster feedback loops and reducing time-to-market.
    • Greater test coverage: AI can generate test cases more comprehensively, identifying edge cases and risky areas that might be missed during exploratory testing.

 2.  AI-driven defect prediction and prioritization:

Tools like DeepCode and CodeScene use AI to analyze code changes and historical defect data to predict potential defects and prioritize areas that require more attention during testing.


    • Focused testing: By predicting high-risk areas, testers can prioritize their efforts on the most critical parts of the application, maximizing the value of their testing activities.
    • Early detection of defects: AI-driven defect prediction can help identify potential issues before they become critical, reducing the cost and effort of fixing them later in the development cycle.
    • Improved software quality: By proactively addressing high-risk areas, teams can reduce the overall number of defects in the software, improving its quality and reliability.

 3.  AI-based visual testing tools:

Tools like Applitools and Percy use AI and computer vision algorithms to automatically detect visual discrepancies and ensure consistent UI/UX across different platforms and devices.


    • Faster visual validation: AI-powered visual testing tools can quickly compare screenshots and identify discrepancies, reducing the time spent on manual visual inspections.
    • Improved UI/UX consistency: By automatically detecting visual inconsistencies, AI can help ensure a uniform user experience across various devices, browsers, and screen resolutions.
    • Reduced human error: Manual visual inspections can be prone to error due to human fatigue or oversight; AI-based visual testing tools can help minimize such errors.

 4.  AI for natural language processing (NLP) in testing:

AI can be used to analyze unstructured data, such as user feedback, bug reports, or documentation, to identify patterns and areas that need improvement. Tools like Adobe Firefly or OpenAI chatGPT can be used to process and analyze text data in software testing.


    • Better understanding of user feedback: AI-driven NLP can help testers understand user feedback better thereby allowing them identify and resolve issues quicker and consequently offer a better user experience.
    • Effective bug triaging: AI can help categorize and prioritize bug reports based on their priority and impact, enabling testers to address the most important issues first. This can also be useful in predicting future bugs thereby allowing defect prevention. 
    • Better test case generation: By analyzing user feedback, bug reports and understanding the patterns, AI can automatically generate test cases that address reported and predicted issues, increasing test effectiveness.

These applications are examples of the many ways AI is already helping improve the testing world by helping improve the efficiency, effectiveness, and overall quality of the software testing process. That said, an important question arises – What are some of the essential skills for integrating AI into testing? Let us speak about the essential skills

  a)  Programming Skills

Testers working with AI-driven tools should possess strong programming skills in languages like Python, Java, or C#. A solid understanding of coding allows testers to customize AI algorithms and develop test automation scripts effectively.

  b)  Data Analytics

AI in software testing relies heavily on data analysis to improve testing strategies and identify patterns. Testers should be proficient in data analytics, including data pre-processing, data visualization, and statistical analysis.

  c)  Machine Learning and Deep Learning

Testers must understand the fundamentals of machine learning (ML) and deep learning algorithms to harness the full potential of AI-driven testing tools. This knowledge will enable them to fine-tune ML models, implement the appropriate algorithms, and optimize the testing process.

  d)  Test Automation Frameworks

To effectively integrate AI into software testing, testers must be familiar with test automation frameworks such as Selenium, JUnit, or TestNG. Proficiency in these frameworks allows testers to create and maintain test scripts that can work seamlessly with AI-driven testing tools.

  e)  Domain Expertise

Domain expertise is crucial for testers to understand the software’s requirements, functionalities, and potential risks. A deep understanding of the domain enables testers to create effective test cases and validate AI-generated results, ensuring software quality.

Having discussed these, now let us look at how companies have taken things to a different level through real-world examples: AI in Action for Software Testing

  a)  The world’s leading OTT platform: Predictive Test Case Prioritization

This platform uses AI-driven algorithms to analyze historical test data and code changes, prioritizing test cases that are most likely to identify defects. By implementing AI in their testing process, the platform has managed to optimize its testing resources and maintain a high-quality user experience across its platform.

  b)  World’s largest social media platform: AI-Assisted Test Maintenance

This platform employs AI to maintain and update their test scripts as their applications evolve. With machine learning models, they can identify changes in the application and automatically update test scripts to remain

That being said we must also be cognizant about what AI cannot do for testers. Here are some situations…

  1.  Human intuition and creativity:

AI cannot entirely replace the intuition and creativity that human testers bring to the testing process. For example, human testers can come up with innovative test scenarios or identify issues that are not easily discernible by AI algorithms, such as subtle usability problems or complex interactions between features.

  2.  Understanding complex business logic:

AI may struggle to fully comprehend complex business logic and domain-specific requirements. As a result, it may not be able to generate test cases that adequately cover all possible scenarios or verify that the application meets all functional requirements. Human testers with domain expertise are better suited to handle such complexities.

  3.  Exploratory testing:

AI is not well-suited for exploratory testing, a type of testing where testers actively explore the application without predefined test cases, looking for defects or areas of improvement. This type of testing relies heavily on human intuition, experience, and adaptability, which are challenging for AI to replicate.

  4.  Handling ambiguous requirements:

AI may struggle to interpret ambiguous or incomplete requirements, which are common in software development. Human testers can use their experience and intuition to make reasonable assumptions and clarify requirements with stakeholders, ensuring that the application is tested effectively.

  5.  Adapting to evolving technologies:

While AI can adapt to new technologies over time, it may not be as quick or flexible as human testers when it comes to learning new tools, frameworks, or platforms. For example, when a new programming language or technology emerges, human testers can learn and adapt more quickly than AI algorithms, which require training data and time to develop new models.

  6.  Emotional intelligence and empathy:

AI cannot replicate the emotional intelligence and empathy that human testers bring to the testing process. Human testers can better understand the end-user’s perspective, identify usability issues, and provide valuable feedback on the overall user experience.

  7.  Ethical considerations:

AI-driven testing may raise ethical concerns, such as bias in algorithms, data privacy, or fairness in testing. Human testers can better navigate these ethical challenges and make informed decisions about the appropriate use of AI in testing.

While AI can assist testers in various aspects of software testing, it cannot fully replace human intuition, creativity, domain expertise, or emotional intelligence. Human testers are still essential for comprehending complex business logic, handling ambiguous requirements, conducting exploratory testing, and navigating ethical considerations. The optimal approach to software testing is likely to involve a combination of AI-driven automation and human expertise.

Embrace AI and use it to assist you and not lead you.
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