AI in Software Testing: Techniques and Best Practices

July 15, 2023

10 min

AI transforms all aspects of software design, development, and testing. New use cases for AI/ML technology are constantly emerging, and companies are finding new ways to incorporate AI tools into various aspects of their operations. With proper planning, AI can empower QA testers to achieve more effective test coverage.

This article describes the concepts behind AI testing and reviews strategies for incorporating it into production systems. It highlights best practices and explores practical use cases.

Summary of key AI testing concepts

Concept	Description
How AI can help in software testing	AI can be used in all aspects of software testing, removing the overhead of repetitive test processes and automating the production and maintenance of testing suites.
Conventional test automation	Some characteristics of conventional test automation include maintenance overhead, siloed testing knowledge, limited coverage, flaky tests, high resource costs, and slow execution times.
Use cases of AI in software testing	There are many use cases for AI in software testing, including automating testing workflows, creating and maintaining test suites, identifying flaky tests, and performing load generation, feature, release, and UI testing.
Types of AI testing	Types of AI tests include regression suite automation, defect analysis, self-healing automation, and code analysis using NLP.
AI testing best practices	Establishing performance baselines, securing test data, using insightful reports and monitoring, integrating with CI/CD, and keeping a human in the loop represent key best practices for implementing AI tests and keeping dev teams and QA teams in close alignment.
AI testing challenges	The heuristic nature of AI systems, the constantly evolving techniques and best practices in AI testing, and the balance between cost and control of AI models represent challenges to overcome in AI testing.

How can AI help with software testing?

You can use AI in every part of an automated test suite. From functional testing—such as unit and controller testing—to nonfunctional and behavioral testing, it can create or update tests in response to new code changes. Development events such as pull requests or CI/CD hooks can trigger these updates. AI testing removes repetitive aspects of the software testing cycle, such as creating and updating tests, which frees developers and QA to focus on actual application errors instead of test suite churn and flaky test results.

One notable area where AI testing significantly reduces developer and QA time is regression testing, which involves creating tests to ensure that code changes do not introduce new bugs or break existing functionality. Regression testing encompasses various tests, including unit, integration, and end-to-end tests. This process is often the most time-consuming aspect of maintaining a test suite.

AI testing also improves the efficiency and accuracy of testing types that require meticulous examination of visual screen elements, such as UI and browser testing.

Conventional test automation

Traditional software testing processes come with additional overhead: You must configure, automate, and update a test suite in coordination with code updates. Tests must be updated or rewritten for all changes to data fields, business logic, or component behavior. These updates lead to lost developer time because test updates add a constant time overhead to feature delivery.

Conventional test automation often focuses on functional testing, simulating expected user workflows and component behavior. As developers introduce code changes, testing processes usually overlook the nonfunctional testing of system characteristics like performance, security, and usability.

Furthermore, traditional test automation can be brittle and prone to false positives/negatives. Minor UI or application flow changes can break existing tests, requiring ongoing maintenance effort. Tests may pass despite underlying issues if they do not cover edge case scenarios comprehensively.

Use cases of AI in software testing

AI is revolutionizing various aspects of software development, including testing. Organizations can enhance efficiency, accuracy, and test coverage by leveraging machine learning, natural language processing, and other AI techniques. Here are some critical use cases of AI in software testing.

Creating test cases for new or updated data fields

As applications evolve and developers add new features, data models often change, requiring corresponding updates to test cases. AI-powered tools can automatically analyze these changes and generate appropriate test cases to validate the handling of new or modified data fields.

AI algorithms can create comprehensive test suites that cover various scenarios and boundary conditions by learning from existing test cases and understanding the relationships between data entities. These algorithms save significant time and effort compared to manually writing test cases for each data field change.

Automation of testing workflows

AI can automate end-to-end testing workflows, from test case generation to test execution and result analysis. You can train machine learning models on historical testing data, application logs, and user behavior patterns to identify the most critical test scenarios and prioritize test execution.

AI-driven test automation frameworks can interact with the application under test, mimicking user actions and validating expected behaviors. These frameworks can adapt to changes in the application’s UI or API, reducing the need for manual test script maintenance. Additionally, AI can optimize test execution by intelligently distributing tests across multiple environments or devices, parallelizing test runs, and minimizing redundant testing.

Ongoing test suite maintenance

As software projects evolve, test suites can become large and complex, making it challenging to maintain and update them effectively. AI can assist in continuously maintaining test suites by analyzing code changes, identifying impacted test cases, and suggesting necessary updates.

Machine learning algorithms can learn from previous test runs, user feedback, and defect patterns to prioritize the test cases that are more likely to uncover critical issues. AI can also help identify redundant or obsolete test cases, optimize test coverage, and reduce test suite execution time.

Identification of flaky tests

Flaky tests exhibit nondeterministic behavior, sometimes passing and sometimes failing without any apparent changes to the code or environment. They can be a significant problem in continuous integration and delivery pipelines, leading to false positives, increased debugging efforts, and delayed releases.

AI can help identify flaky tests by analyzing test execution history, logs, and environmental factors. Machine learning models can learn patterns and correlations contributing to test flakiness, such as network latency, resource contention, or timing issues. Through proactively detecting and isolating flaky tests, AI can help teams address the root causes and improve the reliability of their test suites.

UI testing

Testing user interfaces (UIs) ensures a smooth and intuitive user experience. However, UI testing can be time-consuming and error-prone when done manually. AI-powered UI testing tools can automate the process by leveraging computer vision and machine learning techniques. These tools can analyze screenshots or video recordings of the application’s UI, identify UI elements, and generate test scripts to interact with those elements.

AI algorithms can learn from user interactions, detect visual regressions, and validate the layout, responsiveness, and accessibility of the UI. By automating UI testing, AI helps catch visual defects early and ensures a consistent user experience across different devices and browsers.

Load generation for nonfunctional testing

Nonfunctional testing, such as performance testing, load testing, and stress testing, is essential to assess the system’s behavior under various conditions. AI can assist in generating realistic load patterns and simulating user behavior for nonfunctional testing.

AI models can learn usage patterns, the distribution of requests, and peak load scenarios by analyzing production traffic, user logs, historical data, etc. These models can then generate synthetic loads that resemble real-world traffic, enabling teams to test the system’s performance, scalability, and reliability under realistic conditions. AI-driven load generation helps identify performance bottlenecks, optimize resource allocation, and ensure that the system can handle expected and unexpected loads.

Feature and release testing

Before releasing new features or updates to production, thorough testing is necessary to ensure their quality and compatibility with existing functionality. AI can streamline feature and release testing by automatically generating test cases based on requirements, user stories, or acceptance criteria.

You can use natural language processing (NLP) techniques to analyze textual descriptions and extract relevant test scenarios. AI models can also learn from previous release cycles, identifying high-risk areas and prioritizing test efforts accordingly.

How Qualiti can help

Qualiti is an AI-powered tool that addresses the challenges of test case generation and maintenance. By embedding a script that monitors and records user actions within the application under test, Qualiti collects valuable data in real time. An AI model ingests this data, identifies patterns in user behavior, and continuously learns from new data sets to refine its understanding. As the model becomes fully trained, it can automatically generate and maintain test cases, ensuring optimal test coverage.

The Qualiti dashboard is a centralized platform for users to manage these AI-generated tests and create customized workflows tailored to their testing requirements. This AI-driven approach streamlines the testing process, reduces manual effort, and enables teams to deliver high-quality software more efficiently.

Types of AI testing

AI has opened up new possibilities in software testing, enabling more efficient, accurate, and fortified testing practices. Described below are some key types of AI testing.

All of these types of AI testing boost efficiency, accuracy, and scalability. By automating repetitive and time-consuming tasks, AI allows testing teams to focus on more strategic and exploratory testing activities. AI-driven testing can uncover defects that manual testing may miss, improve test coverage, and provide actionable insights for quality improvement.

Regression suite automation

Regression testing is a critical aspect of software development, ensuring that changes or additions to the codebase do not introduce new defects or break existing functionality. AI can revolutionize regression testing by automating the creation and maintenance of regression test suites. Machine learning algorithms can analyze the application’s codebase, identify critical functionalities, and generate comprehensive test cases that cover various scenarios and edge cases.

These AI-powered regression test suites can adapt to code changes, automatically updating test cases when feature updates (such as property updates, function name changes, and new behavior) are made. This removes the overhead of updating a test suite any time a variable or function is renamed and helps teams maintain a test suite’s relevance over time.

AI-powered regression test suites allow teams to focus on testing the most critical aspects of their systems while trusting that past features will remain stable. By leveraging AI for regression suite automation, teams can reduce the manual effort required for regression testing, catch regressions early, and ensure the stability of the software throughout the development lifecycle.

Defect analysis

AI can significantly enhance defect analysis by employing NLP techniques for code analysis. NLP algorithms can parse and understand the structure and semantics of source code, identifying potential faults, vulnerabilities, and coding best practices violations.

By training machine learning models on large codebases and historical defect data, AI can learn patterns and correlations that indicate the presence of defects. These models can then be applied to new code changes, automatically flagging suspicious code snippets and providing intelligent recommendations for fixes.

AI-driven defect analysis can help developers identify and resolve issues early in development, reducing the likelihood of defects propagating to later stages. Additionally, AI can prioritize defects based on severity, impact, and probability of occurrence, enabling teams to focus their efforts on the most critical issues.

Self-healing automation

One of the challenges in test automation is maintaining the stability and reliability of automated tests in the face of frequent code changes. Self-healing automation, powered by AI, addresses this challenge by enabling tests to adapt and recover from failures caused by code modifications.

When an automated test fails due to a change in the application’s behavior or structure, AI algorithms can analyze the failure, identify the root cause, and attempt to fix the broken test. Fixes may involve updating test scripts, modifying test data, or adjusting test assertions based on the new application state.

Self-healing automation leverages machine learning to continuously learn from past failures and successful test runs, improving its ability to diagnose and resolve issues over time. By implementing self-healing automation, teams can reduce the manual effort required for test maintenance, increase test suite resilience, and ensure the longevity of their automated testing infrastructure.

AI testing best practices

Any test suite will benefit from a combination of the following best practices.

Practice	Description	Techniques
Establish baseline metrics	Define, measure, and track key performance indicators (KPIs) for your AI testing process. Use baseline metrics to identify areas for improvement and optimize your testing process.	Determine metrics such as test coverage percentage, test execution times, false positive/negative rates, and frequency of security risks at volume.
Keep a human in the loop	Have one or multiple team members oversee AI results and document test suite performance over time.	Involve team members in document review, test suite analysis, UX re-review, etc.
Ensure security and data privacy	Be proactive in preventing the leakage of sensitive data in the testing process.	Use techniques like synthetic test data generation, encryption, and access controls to safeguard data privacy.
Use reporting and monitoring	Use ongoing monitoring to check the quality of test suite runs.	Integrate reporting tools into your test suite and monitor regularly.
Incorporate AI testing into your CI/CD pipeline	Integrate AI testing into your continuous integration and continuous delivery (CI/CD) pipeline and automate the execution of AI tests as part of your build and deployment process.	Use commit or PR hooks in source control. Consider nightly, hourly, or weekly builds for larger test suites.

AI testing challenges

AI systems are nondeterministic—they can give different results when run twice, leading to diversity in output across multiple runs. For example, prompting an LLM to generate test data will produce different results per test run. It is important to gauge the correctness of synthetic test data generated by AI models. A well-trained model will consistently generate valid, diverse data across many runs.

The effectiveness of any AI model and the quality of its training are fundamentally linked, and there are tradeoffs between different models. Closed-source models come pretrained, but they incur fees to use. They may also require you to share data with third parties. The alternative is to host—and possibly train—your models and deploy them independently. Training a model gives you more control over how it responds to input, but the costs and time commitments of the training must be considered. The challenge is balancing cost, data, security, and the quality of the results.

QA processes should periodically check model quality to ensure they continue producing high-quality results. Likewise, QA processes must check AI-generated test data for validity and monitor test suite success rates.

Last thoughts

AI is transforming software testing, offering automation, efficiency, and effectiveness. Implementing AI in testing requires careful planning, clear objectives, and a focus on quality. Human oversight and collaboration remain essential, and the value of having dedicated QA and engineering personnel who oversee AI testing operations is high.

Best practices include narrow scopes, repetitive tasks, UI testing, security, and CI/CD integration. While AI automates and augments testing efforts, human oversight and collaboration remain essential. Testers play a crucial role in validating AI results, applying domain expertise, and focusing on strategic and exploratory testing activities that AI may not cover. Fostering close collaboration between AI systems and human testers ensures optimal software deliveries.

AI will continue to revolutionize software testing in the coming years as the field continues to grow and learn. Emerging trends and technologies such as machine learning, deep learning, and computer vision will expand the possibilities of AI testing even further. Organizations that embrace AI in their testing practices can expect improved efficiency, accuracy, and test coverage. Investing in the right tools, infrastructure, and skillsets will provide insight and understanding as new trends emerge.

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