4 Ways to Boost Your Software Testing
Efficiency Boost: AI streamlines testing tasks, impressing all users.
Automation Advancement: AI automates, saving time, familiar with popular tools.
Data Management: AI efficiently handles test data combinations.
Log Analysis: AI assists in identifying errors in test logs.
Transformative AI: AI revolutionizes software testing, aiding all testers.
Feeling overwhelmed by repetitive tasks? Artificial intelligence has made a dramatic entrance into software testing. It arrived swiftly, catching many by surprise, and has brought about significant changes. Although AI is unlikely to replace human testers soon, it is poised to greatly enhance their productivity.
Soon, AI will assist with all aspects of testing, from strategizing to execution, to load testing and UX testing. While we await the full range of these capabilities, here are some immediate applications you can explore with any AI chat application or an AI copilot integrated into your testing tools.
Designing Tests With AI
Start by asking AI to help design your tests. Describe a UI page verbally or upload a screenshot. Inquire about considerations for testing the page. For complex tests, describe the user story or business process, which usually outlines the tester’s happy path. If the AI’s response meets your expectations— and it likely will—ask it to generate a set of test scenarios based on that advice.
Experienced testers will be surprised at the efficiency AI brings, handling the bulk of routine work. Novice testers will find AI’s assistance invaluable, offering a significant learning opportunity.
Test Automation Assistance
With your test scenarios ready, explore how AI can automate them. You’ll likely be impressed by the results. AI is familiar with several popular automation tools. While more complex scripts might not be perfect, they will still save considerable time.
If you have an existing automation script written by someone else, ask AI to explain it. This might not work with proprietary tools, but when compatible, it’s a significant time and effort-saver.
Handling Data Combinatorics
Data combinatorics involves identifying all relevant test input data for different fields or API parameters and combining them efficiently to cover various data combinations without creating an excessive number of tests.
Input the data and ask AI to manage the combinations. In cases where you don’t have the data, AI can generate it for you. For example, I recently tasked AI with creating contact information for a dozen individuals in different U.S. states, which saved time and avoided monotony.
Analyzing Test Logs
A more challenging application involves analyzing test logs from long automation scripts. Feed the test log to AI and ask it to identify any errors and draft bug reports based on the findings. The results may vary, but it will give you further insight into what AI can currently achieve in testing.
AI is set to revolutionize how testers work, enhancing the capabilities of adept testers, accelerating the learning curve for novices, and even helping less diligent testers achieve more by focusing their efforts on high-value testing activities.
These adjustments aim to improve the readability and impact of your message, highlighting the transformative potential of AI in software testing.
For more on software testing, subscribe to the QA Lead's newsletter for insights delivered straight to your inbox.
