How AI can change Software Testing?

Artificial Intelligence (AI) and Machine Learning( ML) have graduated from mere technical concepts and tools to full-fledged buzzwords. They have inspired everything from great films to philosophical debates. More importantly, they have allowed incredible advancement in every possible human activity, medicine or gaming.

In such an atmosphere, discussing the effect of AI automation in testing is essential. What role can AI play in Functional test automation?

Let’s find out.

The Challenge

It is common for QAs to spend significant time ensuring that new code does not break the existing codebase. Each time developers add new code, new tests must be carried out. Regression testing cycles are extremely time-consuming when undertaken manually and can burden QAs to a large extent.

• Traditional QA methods involve checking off a list of tasks that ensure the software works how it is meant to. Initially, this is feasible when only a few features have to be tested.
• But as features increase, QAs face difficulty meeting deadlines while performing comprehensive tests.
• The more complex an application, the more difficult it is to ensure it receives full test coverage.

Automated Testing has gone a long way in assuaging the challenges of manual testing. Repetitive tests such as regression and sanity tests can be automated via multiple tools, most notably Selenium WebDriver.

However, with the advent of AI, the possibility of streamlining the testing process has magnified. Due to competition, new software and product launches must be accelerated since extended deadlines are not an option. Thus, there is no option but to test smarter rather than harder.

How can AI optimize Testing?

1. Accelerating Timelines

• Instead of going through thousands of lines of code, as manual testers have to do, AI can quickly sort through log files, scan code in seconds, and detect errors in much less time.
• Additionally, AI does not get exhausted or make human errors, thus yielding more accurate results.
• By incorporating AI into repetitive tests, QA engineers can focus on testing new features or paying particular attention to the software’s essential parts.

2. Better Automation

As mentioned above, a large part of a QA’s job is to ensure that new code does not destabilize existing functional code. As more features are developed and added, the amount of code to be tested expands and can overwhelm already-burdened QA engineers.

• AI bots, on the other hand, will be able to evolve with changes in the code.
• They can adapt and learn to identify new functions.
• When AI bots identify a modification to the code, they can be programmed to decide if it is a new feature or some defect emerging from changes to the code.
• It is also possible to improve automated testing by utilizing a built platform.
• AI in visual testing is also catching up and evolving rapidly with more sophistication in change detection.

BrowserStack, for example, provides a robust, 24/7 available Selenium grid of 2000+ real browsers and devices for testing. By leveraging such a grid, QAs can accelerate testing thanks to features like parallel testing. Additionally, by testing in real user conditions, QAs can ensure that the website or app is free of all possible bugs that might tarnish user experience.

3. Clarity on Client Requirements

In the realm of software testing, being able to understand what the client wants is of paramount importance.

• Using AI in software testing, examining similar websites and apps to determine what contributes to success among the target audience is possible.
• AI can help study competing products to identify key selling points so that developers and testers know what users want from a particular software.
• Testers can use AI to detect common code flaws that might hinder a software system’s flawless functioning.
• By understanding what the client wants, they can create test cases to ensure the product does not break when achieving those goals.

4. Mastering Test Automation with ChatGPT

As part of the automation testing trends in 2023, ChatGPT has taken the world by storm. Regarding AI for software testing, OpenAI is a state-of-the-art language model using natural language processing-based artificial intelligence that allows for the automation of tasks.

It can generate text in different formats, such as structured data, code snippets, and annotations, which can be used to automate tasks such as code generation and testing.

ChatGPT for test automation can be used for:

• Code Generation: it can generate code snippets based on natural language prompts, providing an excellent way for beginners to learn a programming language’s basic syntax and structure.
• Code Completion can be used to complete partially written code, assisting with the next steps.
• Code Explanation: it can be used to explain the purpose and function of specific lines of code, helping beginners and experts alike.
• Debugging: it can identify and explain code errors, helping beginners learn how to debug and troubleshoot their code.
• Project Guidance: it can provide advice and suggestions for project structure, best practices, and libraries for beginners who want to build a project.

AI software testing will help improve testers’ lives by introducing greater accuracy and speed. By detecting bugs quicker and faster, AI can give testers the time and mental energy to create better testing methods, write better test scripts, and devise ways to craft the best possible user experience.