Artificial Intelligence in Software Testing
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How to Apply AI in Software Testing?

07 Jul 2021

One of the most exciting areas in testing and quality assurance (QA) at present is the potential influence of artificial intelligence (AI) and machine learning (ML). Imagine, what if the software could learn to do all the testing itself? What if it could automatically track down and weed out bugs?

This is not just imaginary thinking but a vital solution in software testing to incorporate automation that relieve teams from the burden of repeating and refining testing. The rise of AI is now powering certain methodologies with machine & deep learning algorithms.

You would agree while traditional test automation has enabled teams with a smarter and quicker means for delivering quality, AI-powered tools can drive its capabilities to the next level. And, if you’ve been waiting out to learn more about the specifics of ML and AI in software testing then don’t go anywhere else, we got you covered!

Table of Contents:

AI is focused on making software testing smarter, more effective and efficient. With smart technologies and tools, it is important to apply reasoning / problem solving to automate testing & accelerate the software development process. Further, this helps reduce mundane and tedious manual efforts, so teams can focus on developing innovative features by relying on the test results.

Research shows that almost 90% of respondents will adopt testing with AI as part of their growth strategy.

Evolution of Intelligent AI-Driven Software Testing

The paradigm of software testing has evolved significantly over the past two decades. Right from manual to automation, where selenium was considered to be one of the finest tools, the testing journey has been quite promising.

With the dawn of AI-based testing, it has now become very impactful with the use of autonomous agents referred to as “test bots.” These intelligent agents help automate activities such as application discovery, modeling, test creation and failure identification. Their implementation is performed by combining different ML techniques including neural networks, tree learning, reinforcement learning and more.

AI-Driven Testing Approaches

AI-Based Testing Approaches
  • Differential: Helps test different versions of similar applications. Carries out a comparison to understand differences, versions overbuilds and learn from classification feedback.
  • Visual: Image-based testing needs visual validation. With pattern and image recognition capabilities together, it helps detect visual bugs to test the look and feel of an application.
  • Declarative: Aims to specify test intent in a natural or domain-specific language. It lets the system decide on how to conduct the test.
  • Self-healing Automation: Helps auto-correct elements in tests with the change of user interface (UI).

The above-listed testing approaches help save time from days to mere hours which translates directly into cost savings. Interestingly, automated tests produce quality websites and applications.

Key Challenges of Using AI in Software Testing

The biggest challenge with AI-based testing is about choosing the right type. Of course, the optimization does not apply to all the models. Hence there’s a need for an enhanced setup and complex flow.

The other challenges of AI-based test automation include:

  • Researching the exact use cases
  • Lack of complete awareness of what actually is expected.
  • Validating the actions of the applications based on the input.
  • Analyzing the app for performance, functionality, scalability, security and stability.

From enhancing accuracy to tackling manual testing constraints, AI-based integrated solutions can considerably improve the overall process. This helps you to produce effective results, much faster. Setting up a dedicated system might have some initial investment, but over the long haul, it will certainly help you save effort and cost.

Benefits of AI in Software Testing

The application of Artificial Intelligence enables taking automation to a new level where the testers don’t need to deal with repetitive tasks anymore. Here are the core benefits:

Why AI in Software Testing?
  • Improved Accuracy:
    • Repetitive tasks can be handled more quickly and efficiently without much effort
    • Accurate reporting with detailed result outcomes
    • Eliminates the minute chance of errors
    • AI takes impressions of failures on debugging logs and identifies duplicate defects.
    • Enhances the overall test accuracy
  • Faster Time to Market:
    • Automated tests assist in shortening the development and verification timelines.
    • Developers tend to spend less time writing code and test engineers spend less time running actual test cases.
    • The test input gets implemented after each source code modification
    • Decreases any additional cost involved in operating recurring test cases.
  • Enhances Test Coverage:
    • Leveraging AI for testing helps QA engineers to get insights about file contents, memory usage, internal program statistics, data tables, files storage and content structure.
    • Enables performing various distinctive test cases to examine the database behavior as per the expected outcome.
  • Executes Visual UI Testing:
    • Image and pattern recognition capabilities enable AI to recognize dynamic UI controls

Further, pattern recognition and machine learning improve the flexibility and accuracy of software tests. AI-based testing allows computing systems to validate more tasks that earlier needed human intervention.

Ready to Unlock the Potential of AI-Led Automation?

Let’s harness the power of next-gen testing technologies to drive optimum value for your organization.

AI-Based Software Testing Tools

  • Functionize: A cloud-based automated tool that leverages ML for performance, function and load tests purposes. The tool utilizes intelligence capabilities to improve test creation, analysis & maintenance.
  • Appvance: This tool uses AI technology to create test cases depending on user behavior. The test portfolio depicts what real systems will perform on the production systems. It helps enhance the quality, performance & security of the application.
  • Testim: Automated functional tool uses ML to write, execute and maintain tests. It allows users to create E2E (end-to-end) tests that are coded, codeless, or both.
  • Testcraft: AI-driven automation testing platform for continuous and regression testing. It is also used to monitor websites or web applications. Using this tool, testers can visualize automated Selenium-based tests through a drag and drop interface and run them on various browsers and work environments.
  • Applitools: An automated intelligence platform for software monitoring, visual user interface tests and visual management. This is one of the widely used AI testing tools that scan app screens and analyzes them just like humans’ brains by identifying potential defects and bugs. It is widely used in DevOps, manual QA, digital transformation and test automation.

Our team of proficient QA & Software Testing professionals has hands-on exposure of the above-listed tools. As a next-gen QA and testing services provider, we can help you to get faster and quality releases with more efficiency and accuracy.

How AI is Impacting Various Software Testing Applications

It is clear by now that AI in test automation helps address the limitations of traditional automated testing to deliver enhanced value. It enables organizations to validate quicker and better while reducing human dependencies and costs.

Let’s take a closer look at some real-life applications:

  • Unit Testing: With AI-enabled RPA tools, testers can reduce flaky test cases while performing unit testing. It also helps manage unit test scripts maintenance.
  • API Testing: Test automation tools leverage AI to convert manual UI tests into automated API tests. It uses reasoning to recognize the relationships and patterns in a series of API calls while exercising UI. By analyzing the user behavior, the machine intelligence can create more advanced inputs and patterns for verifying the API.
  • UI Testing: Ensures more accuracy in comparison to manual testing. It is hard to manually detect parameters such as size, layout, Graphical User Interface (GUI), and colors, which can be easily identified with AI.
  • Continuous Testing: As part of the continuous testing method, software professionals can determine modified controls with frequent algorithm updates.

The Future is Now: AI and ML In Testing

To conclude, Artificial Intelligence is transforming the testing landscape. Organizations will continue to go above and beyond to optimize and enhance their software development life cycle with an accurate and effective mechanism.

Using advanced AI and ML-driven analytics, Rishabh Software can help unlock the power of mission-critical data including software defect logs, project documentation, assessment results, testing artifacts and more. Our team of AI testing specialists puts to use supervised as well as unsupervised advanced software testing methods to help you increase efficiency, release better quality products and get to market faster.

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Our specialists will dive into your current business model to fast-track your digital transformation journey with AI-led testing.