AI in Software Testing: How to Use it Most Effectively
Teams building software for industries like banking, healthcare, aerospace, and infrastructure are feeling more pressure than ever. They need to improve efficiency to avoid falling behind competitors using AI, but at the same time, enhance risk management to avoid costly and dangerous mistakes from these same technologies. AI isn’t just a buzzword here — it’s a force multiplier that helps QA teams move from “find-and-fix” to proactive quality engineering.
Today, we’re going to cover the recent advancements of AI in software testing, what concrete advantages it brings, tools your team can use, and specific ways to incorporate AI into your stack and workflows for efficient (but compliant) development.
Key Takeaways:
- Embed AI into your existing workflow, don’t bolt it on. Teams get the most value from AI when it’s native to their test management and automation tools instead of an afterthought that causes friction and slows adoption.
- Reduce test brittleness and maintenance with self-healing and adaptive tools. Spend less time manually updating scripts and other areas when your app’s UI changes or shifts.
- Prioritize tests based on risk, coverage, and value rather than trying to automate everything. Not all tests are equally valuable, so leverage AI tools to perform risk analysis earlier and identify coverage gaps for urgent testing.
- Human oversight is still essential, especially for domains with high compliance, complex integrations, or edge cases. Hallucinations, lack of context, and other issues are still significant concerns for many industries and projects.
Why is AI so Impactful in Software Testing?
The primary reason that AI is so relevant and top-of-mind for software QA and testing is that it hits exactly where manual approaches break, including large-scale projects, pattern recognition, and ambiguity. Modern applications and systems need to function on more platforms and environments by the day (browsers, configurations, integrations, etc.), which human-written tests simply can’t keep up with.
By using AI techniques like NLP, computer vision, and machine learning:
- Test cases can be generated at scale
- Smart element-locators can adapt to ongoing UI changes
- Predictive risk scoring can prioritize key focus areas
These enhancements affect QA more than most other areas in the SDLC, meaning that testing teams are being pushed to implement AI more and more.
Role of AI in Software Testing: An Assistant
The primary value that AI provides to software testing right now is assistance with tedious tasks. AI can read your requirements, ideate test designs, write or repair scripts, and highlight risky areas of the codebase. This frees up dev time to focus on higher-complexity areas instead of being bogged down with repetitive activities that AI can automate.
Inflectra.ai is a prime example of how AI can play the role of assistant to enhance your QA. It is built into our Inflectra suite of tools (e.g. Spira and Rapise), natively generating artifacts like test cases and requirements from user stories or other existing artifacts. Inflectra.ai acts as an extension that puts generative AI capabilities at your fingertips without having to leave your workflow.
How Will AI Impact Software Testing?
In the next 12-24 months, we expect a broader adoption of AI for test generation and improved self-healing capabilities. Teams currently use AI to automate the tedious parts of QA (which Inflectra.ai is already starting to deliver), so this will likely continue.
In the next two to five years, AI in software testing will go deeper via agentic systems that run continuous exploratory sweeps, propose fixes, and more closely integrate with observability. However, governance, explainability, and compliance will all determine how quickly enterprises and certain industries will adopt these advancements.
Beyond five years becomes much more blurry, but we anticipate that AI will eventually automate entire regression cycles and augment developers and testers with contextualized recommendations across the entire SDLC.
How to Use AI in Software Testing: 8 Ways to Integrate AI
1. Test Case Generation
One of the most popular ways to use AI in software development is to create test cases at scale. Often, tools can take a user story or requirement, suggest and create test cases based on that information, and refine the test cases to minimize any necessary manual edits. Tools like Rapise can then convert these test cases into executable tests, further reducing authoring time and improving traceability.
2. Enhanced Automation
Although automated testing tools have been around for years, AI has pushed automation to the next level. These new systems can translate human-readable steps into scripts, suggest assertions, and create data-driven variations. For example, Rapise provides intuitive natural-language-to-code and even screenshots-to-steps features that accelerate your automation beyond traditional test automation tools.
3. Low-Code (NLP) Testing
Testing has historically been dominated by developers who understand how to code test cases and scripts, but AI is making NLP and no-code testing more accessible. This means that non-technical users can describe scenarios in plain English and tools can convert the descriptions to test artifacts and executable scripts. The result is democratized automation, leading to more cross-team collaboration and input on testing.
4. Self-Healing Tests
As your applications and interfaces change, test scripts need to be kept up-to-date. Instead of manually updating each element when a button moves or changes, vision-based locators can repair failing selectors automatically. For example, Rapise’s self-healing mode and full-path locators help QA teams reduce brittle tests and tedious manual fixes.
5. Test Data Generation
Depending on the project, test data might be in short supply — however, lack of data can hamper the effectiveness of your tests. AI systems can help generate localized and bounded synthetic data that matches your requirements, simulating more realistic conditions and improving test coverage. Generated data can also bolster your data privacy and security by de-identifying sensitive information.
6. Risk Analysis
AI pattern recognition and contextual understanding of business goals are increasingly being used to evaluate risks. From historical defects and code churn to requirements complexity, these tools can prioritize testing areas more effectively than most human testers. For example, Inflectra.ai produces risk assessments that are tied into Spira’s planning and test selection, so you run the right tests at the right time.
7. Root Cause Analysis
Another way to leverage AI’s pattern recognition and other advantages is in analysis, summarizing failure traces, clustering similar failures, and suggesting probable root causes. From there, AI can help file triaged issues directly into your defect management system for developer action. Note: LLM outputs should be used for diagnostic help, not final verdicts on root cause analysis.
8. Regression Automation
Regression testing is a critical part of QA and development, mean that it’s another key area where AI can support. Tools like Spira and Rapise help automate the high-value regression suite, keep tests self-healed, and orchestrate large runs to validate PRs and nightly builds. We recommend using risk-driven selection to avoid running everything with every commit, which will waste CI resources.
Best AI Tools for Software Testing
Currently, there are three broad categories of AI tools in software testing:
- General-Purpose LLMs: ChatGPT, Claude, Gemini, etc.
- AI-Powered Extensions: Zephyr, Testomat.io, etc.
- Integrated AI QA Solutions: Inflectra’s suite of Rapise, Spira, and Inflectra.ai
Below, we’ll look at the more common questions and approaches that software testing and QA teams have been using AI for in 2025.
Can You Use ChatGPT for Software Testing?
Yes, ChatGPT is useful for generating test ideas, writing unit or API test skeletons, writing BDD-style scenarios, and more. However, we recommend treating it like an intern-level assistant, because we’ve found that even recent releases like GPT-5 and GPT-4o still frequently hallucinate and produce errors.
Prompt Examples
Some example prompts you can use for ChatGPT software testing include:
- “Given this user story: [paste story], generate 10 boundary and negative test cases and map them to acceptance criteria.”
- “Write a test script that logs into this example app using these credentials and verifies invoice export.”
- “Create 20 synthetic user profiles for performance tests (countries, locales, edge-case names, invalid emails).”
- “Identify the system areas that should be included in upcoming regression testing after changes are made to [paste function/module details].”
- “Generate a detailed bug report for this defect: [paste bug details]. Include bug ID, steps to reproduce, expected vs. actual results, severity, priority, environment details, and potential impact on users.”
- “Given these requirements: [paste list of requirements], and test cases: [paste list of test cases], find gaps and areas where coverage is not adequate. Suggest additional tests to ensure comprehensive test coverage.”
Can You Use Claude for Software Testing?
Yes, Claude is also useful for test design, prompt engineering, and analysis. We’ve found that Claude models like Opus 4.1 and Sonnet 4 have better reasoning, safety features, and accuracy than OpenAI models, but still have limitations inherent to being a general-purpose LLM. Claude can be used in software testing to produce test artifacts, perform log analysis, and generate bug reports.
Prompt Examples
If you’re looking to try Claude for software testing, here are some useful prompts:
- “Analyze these failing logs and summarize the most probable root cause with suggested next debugging steps: [paste logs]”
- “We have logs of test failures over the last 30 days: [paste logs]. Cluster similar failures, suggest probable common root causes, and propose mitigation steps.”
- “Given this API spec: [paste API], create contract tests for both success and error paths.”
- “Suggest edits for this flaky Selenium script to improve its reliability: [paste script]”
- “Generate a skeleton automation test for the following scenario using [paste tool name]. Scenario: A user logs in, navigates to the invoice section, filters by date range, and exports a PDF. Include page-object or modular structure, assertions, and error handling.”
Inflectra’s Suite (SpiraTest, Rapise, & Inflectra.ai)
Using an integrated and purpose-built ecosystem of tools (like Inflectra’s) provides more capabilities and flexibility to adapt to your QA workflows. Tools like Rapise and SpiraTest can perform the tasks that the ChatGPT and Claude prompts listed above do, but the dedicated tools are more streamlined and integrated with your existing workflows and tools.
- Spira provides end-to-end requirement, test, and defect management that uses built-in AI features to enhance automation and risk mitigation.
- Rapise is an AI-driven, codeless test automation platform that can create self-healing tests that work across desktop, web, mobile, API, and ERP testing.
- Our Inflectra.ai tools have been integrated into Spira and Rapise to further extend their AI capabilities, such as generating test cases from user stories, reviewing requirements for syntax, suggesting risk assessments, and automating other repetitive content generation from within Spira.
What are the Benefits of Using AI in Software Testing?
There are a variety of business and development benefits of incorporating AI into your software testing, and we’ve compiled the most relevant into the list below:
- Faster Test Execution: AI helps prioritize the highest-value tests, enabling more efficient parallel runs.
- Automation for Non-Technical Users: Codeless frameworks make it easy for product managers, QA analysts, and non-developers to describe tests in natural language for cross-team test contribution.
- Less Test Maintenance: Self-healing tests make your pipeline less brittle as UI evolves, leading to fewer rewrites and less script upkeep.
- Better Test Coverage: AI can suggest test scenarios based on patterns that humans may have missed and even generate synthetic test data to expand your coverage of edge cases.
- Deeper Predictive Analytics: As models advance, their predictive analytics capabilities go deeper into defect forecasting, risk scoring, and triage preparation.
- More Efficient Security Testing: Key techniques like fuzz testing require large-scale data inputs, which AI is extremely effective at generating to speed the process up.
- Unscripted Bug Hunting: Newer agentic or vision-based AI systems can run unscripted exploration to surface unexpected behaviors, complementing human exploratory testing.
- Significant Cost Savings: All of these advantages (e.g. less maintenance, faster testing, higher defect detection, etc.) reduce time-to-fix and the number of field incidents for better ROI.
When Should You Use Manual Testing vs. Traditional Automated Testing vs. AI Testing?
With the evolution of software testing from manual testing to automated testing to AI testing, each has different characteristics and use cases.
|
Factor |
AI-Assisted Software Testing |
||
|
Primary Strength |
Human judgment and exploratory insight |
Repeatable regression across environments |
Scale, pattern recognition, generation, and repair |
|
Speed |
Slow |
Moderate authoring speed, fast execution |
Fast authoring, fast execution |
|
Maintenance Burden |
Low on scripts (none), high on coverage |
High (fragile when not using self-healing automation) |
Moderate |
|
Required Technical Skill |
Domain and exploratory skills |
Development and QA engineering |
Lower barrier (but still requires export oversight) |
|
Compliance/Trust |
High if human-validated |
High if scripted & validated |
High if AI outputs are reviewed & traceable |
Use Manual Testing for Exploratory Testing
Manual software testing is best for exploratory testing, UX/acceptance reviews, compliance checks that require human judgment, or other scenarios where human intuition and context matter most.
Use Traditional Test Automation for Repeatable Workflows
Automated software testing is best for stable and repeatable workflows where scripted tests are efficient and predictable, like API contracts, nightly regressions, and more.
Use AI Testing for Changing Systems
AI-powered software testing is best for quickly evolving UIs, large combinatorial spaces, and projects that need high coverage with low maintenance. This testing can also help accelerate test authoring and triage, but should be combined with human oversight for critical paths.
Will AI Replace Manual Testers?
While AI continues to automate and take on more of the testing process, we do not believe that it will fully replace manual testers. However, the role of human testers will change and evolve as AI takes over the more repetitive and high-volume tasks like authoring, maintenance, and data generation. This will free up human testers and quality engineers to focus on mission-critical exploratory testing, complex integration scenarios, compliance, and quality strategy.
At Inflectra, our approach to AI testing reflects these changes. Inflectra.ai is embedded directly within Spira and Rapise, so human-led QA is augmented instead of being replaced. This is similar to a recent discussion about whether AI is killing Agile development, which is also changing how its roles operate.
Learn how AI is forcing Agile development to evolve here.
AI-Driven Test Enhancement: How Inflectra Keeps Your QA Ahead of the Curve
The last few years have made it clear that AI in software testing is no longer hypothetical. These tools continue to improve test coverage, reduce maintenance costs, and accelerate releases for higher-quality software delivery. Inflectra provides industry-leading solutions that keep your QA and development teams on the cutting edge and ahead of competitors.
- Inflectra.ai generates artifacts, risk assessments, and suggested tests from within your Spira workspace so traceability remains intact.
- Rapise turns those artifacts into maintainable AI-assisted automation with self-healing test generation and natural-language authoring.
- SpiraTest ties this together by managing your test artifacts, running and orchestrating automated suites, and centralizing reporting so QA output drives business decisions.
Are you an enterprise or large organization that needs these capabilities on a larger scale? SpiraPlan is our project and portfolio tool designed for enterprises and built with the same AI-enhanced features.
Learn more about our platforms and how they enhance modern software testing:
