AI technology has transformed software testing practices by establishing new performance standards for speed, accuracy, and scalability. What started off as an autocompletion feature within integrated development environments (IDEs) has been transformed into assistants that produce complete test automation scripts, identify test failures, and interpret user stories to generate suitable test cases.
A six-week trial of GitHub Copilot at ANZ Bank involved 1,000 engineers who reported improved productivity and code quality. The data from ANZ Bank demonstrates a wider industry pattern because Gartner forecasts that AI code assistants will reach 75% adoption among enterprise software engineers by 2028, up from less than 10% in early 2023.
The article explains how to choose, evaluate, and successfully implement AI coding assistants tailored to modern QA practices.
AI coding assistants have become fundamental tools for software engineering and QA teams. These tools allow developers to create and sustain code through sophisticated language models that grasp context and purpose beyond basic syntax recommendations.
These tools provide context-sensitive suggestions for writing and code maintenance. Think of it as an automated pair programmer that continuously assists with code writing, review, and improvement during real-time development. These assistants differ from conventional IDE suggestions in their capabilities.
AI coding assistants:
General-purpose assistants: GitHub Copilot or Tabnine: Great for developers across the board.
Test automation-focused assistant: Parasoft Selenic or Tricentis Testim: Designed with QA in mind.
How AI Coding Assistants Help QA Teams
Testing teams are pressured to handle more tasks with reduced resources. AI coding assistants offer a scalable solution to decrease manual work while expanding testing capabilities. The tools deliver maximum value in complex test framework environments or high-speed release cycles.
AI coding assistants assist developers who write tests in Java or TypeScript using Selenium, Cypress, or Playwright through the following features:
Faster script creation: Generate test cases with minimal input
Flakiness fixes: Suggest more stable locators automatically
Boilerplate reduction: Handle setup, teardown, and assertions
Cross-platform support: Web, mobile, and API
The GitHub 2024 Open Source Survey shows that AI-powered tools are gaining speed in developer ecosystem adoption. The survey reports that 73% of open-source contributors use GitHub Copilot and other tools to help with coding and documentation work.
Real-World Implementation of Coding Assistants
Implementing AI coding assistants requires decisions at the leadership level. Teams need training, process alignment, and buy-in to achieve value. The implementation process becomes most straightforward when it matches developers' existing work practices.
Where do these assistants fit:
Tip: Use assistant-generated code in peer reviews to ensure human validation.
Selecting the appropriate AI assistant requires more than technical specifications. The assessment should evaluate how well the tool integrates with the technology stack and QA workflows and meets compliance requirements and future development plans. Look for tools that:
Support Selenium, Cypress and Playwright frameworks.
Operates effectively with Java and TypeScript programming languages.
Outputs provide useful information that is both safe and explainable.
Are ready for enterprise use with Single Sign-On functionality and compliance standards such as SOC2 and HIPAA.
Have a pricing model that offers fair seat-based costs or usage-based plans.
Top AI Coding Assistants in 2025
AI coding assistants have evolved in their capabilities and market relevance. Here are the tools that QA teams and engineering leaders are choosing in 2025. We’ve categorized them by strengths, languages, and suitability for test automation.
GitHub Copilot
Best For: Test generation in development IDEs
Language Support: Java, TypeScript, and many others
Key Features: Code completion, test boilerplate, edge case suggestions, code review, and AI model access
Pricing/Notes: Free (limited requests), Pro $10/mo, Pro+ $39/mo, Business $19/user/mo, Enterprise - $39 per seat
Amazon Q Developer
Best For: AWS-native testing and cloud QA
Language Support: Java, Python, TypeScript, and more
Key Features: Secure coding, AWS-aware completions, reference tracking, and security scans
Pricing/Notes: Free: Individuals, Professional: $19/user/month
Parasoft Selenic
Best For: Selenium test creation and healing
Language Support: Java (JUnit/TestNG)
Key Features: Self-healing locators, test impact analysis, AI-powered recommendations, and CI/CD integration
Pricing/Notes: Basic: $99/month (5 users), Essentials: $199/month (10 users), Enterprise: Custom
ChatGPT
Best For: QA upskilling, and debugging help
Language Support: All major languages
Key Features: Conversational code writing, debugging, data analysis, and image/video editing
Pricing/Notes: Plus, the plan requires: ~$20/month
Tabnine
Best For: On-prem or secure enterprise setups
Language Support: Multi-language
Key Features: Local model support, fast completions, and privacy-focused
Pricing/Notes: Free: Limited, Enterprise: $39/user/month
Codeium
Best For: Budget-friendly, team-wide rollouts
Language Support: 70+ languages
Key Features: Free autocomplete, VS Code/JetBrains plugins, and in-editor AI chat
Pricing/Notes: Free: Limited, Teams: $30/user/month, Enterprise: $45/user/month
AI assistants are already delivering tangible results across test organizations. These use cases prove particularly effective in driving down cost and cycle time while improving test resilience.
Automate Boilerplate and Setup Code: Automating test setup and boilerplate code generation saves time and minimizes errors when configuring environments.
Generate Web UI Test Scripts: This feature enables users to generate UI test scripts for Selenium, Cypress, and Playwright through prompts or comments.
Accelerate Mobile Test Automation: Simplifies mobile test scripting by automatically completing Appium setups and gestures, including taps and swipes.
Automate API Testing Scenarios: Enables users to create REST API test cases, which include authentication features and response validation through standard libraries.
Suggest Better Assertions and Edge Cases: Provides relevant assertions and edge cases that improve test coverage and reveal defects.
Generate Page Object Models and Locators: Creating page object models and locators improves reusability and reduces test flakiness.
Create Realistic Test Data Instantly: Produces authentic test data that can apply to forms, APIs, and load-testing scenarios.
Refactor and Reuse Test Logic: Transforms duplicated test operations into reusable functions, producing cleaner and more maintainable codebases.
Improve Test Documentation and Onboarding: Provides automatic comments and summaries of test logic to enhance readability while assisting new team members during onboarding.
Fix Flaky Tests with AI Guidance: Detects flaky tests through recommendations for improved selectors, waits, and retries logic detection.
AI tools are powerful, but they’re not magic. Success depends on responsible adoption. Teams that accept AI suggestions without question can end up with test debt and create new bugs.
Blind trust: AI-generated code can be wrong, and code review must be done before committing
Security exposure: Avoid uploading sensitive code to cloud-based tools without due diligence
Debugging complexity: Generated code can be complex to trace if poorly commented
Overdependence: Don’t skip test strategy planning just because tools are smarter
Teams can now use plain English to describe user flows, which instantly produces test scripts through NLP-to-Test Automation.
AI bots now perform test writing, execution and analysis tasks with only small amounts of human direction.
AI algorithms now assist in determining test priorities through risk assessment and analysis of usage data and defect trends.
According to Gartner, AI software spending will grow to $297.9 billion by 2027, and coding assistants' capabilities will improve further in the coming years.
Frequently Asked Questions
1. Are AI coding assistants really saving developers time?
Teams achieve 20–40% faster test creation while reducing maintenance cycles.
2. What are AI coding assistants?
The tools provide code generation capabilities through context-based and natural language input processing.
3. What is the best AI assistant for coding?
The general-purpose GitHub Copilot tool pairs with Parasoft Selenic for Java test-focused teams.
4. What is the future of coding with AI?
The evolution of AI technology will shift from assisting in performing independent tasks, such as risk analysis and defect triaging, to assisting in performing these tasks independently.
5. Can AI replace QA engineers?
No. It supports, not replaces. Human beings maintain superior abilities in judgment and business understanding, and coverage strategy development
6. Do AI tools work with Selenium and Playwright?
The tools Copilot, Codeium, and Amazon Q Developer function effectively with both Selenium and Playwright.
7. Are these tools secure for enterprise use?
Select assistants that provide private models and SOC2 compliance and on-premises options, such as Tabnine.
8. Can assistants help with test flakiness?
Absolutely. The testing tools, like Selenic, provide automatic locator fixes when UI components change.
9. How do we train our team to use these?
Start with a pilot. Offer a 1-hour workshop. Testers should perform experiments in a restricted testing environment.
10. What’s the ROI of adopting one?
Faster test creation, lower maintenance, and quicker onboarding, often visible within 1–2 sprints.
Final Thoughts
AI coding assistants transform test automation by speeding up script development, decreasing maintenance needs, and enhancing code quality. Enterprise adoption of these tools continues to grow, and their capabilities evolve to support QA teams meeting current delivery requirements. Success depends on proper implementation, human supervision, and tool alignment with the testing workflow.
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