AI in testing: from manual checks to a smart workflow
My name is Dzianis Talstsiuk. I am a QA Engineer at EXANTE, an international brokerage platform for trading on global financial markets. I am responsible for the desktop trading terminal, the web terminal and the mobile applications. This article covers how I built AI into our testing processes, using it as a working partner alongside a tester. The assistant speeds up testing, broadens test…
Dzianis Talstsiuk, a QA Engineer at EXANTE, an international brokerage platform for trading on global financial markets, describes how artificial intelligence (AI) was integrated into their testing processes to accelerate the delivery of features. EXANTE operates in the high-quality domain of the financial industry, where inaccuracies in prices or unresolved orders can lead to client losses and regulatory risks.
The company employs a shift-left approach, involving testers in the early stages of development to review specifications and acceptance criteria. This approach enables early detection of defects, reducing rework, and speeding up delivery.
AI is applied to various stages in the testing process, from requirements analysis to automated testing. The tools and stack for testing the trading terminals consist of Python and pytest for automated tests, Playwright for web terminal testing, OpenCV, EasyOCR, and PyAutoGUI for desktop application testing. For AI tools, EXANTE utilizes solutions from Anthropic and OpenAI.
The implementation involves installing an extension and logging into the system. However, the focus of this article is on an approach that integrates AI into the process itself.
The QA entry point in a classic process involves involving testers in requirements analysis to review specifications, validate acceptance criteria, and identify gaps at an early stage. However, setting aside enough time for a deep review of the specification within a sprint can be challenging, leading to reactive testing instead. The traditional flow often results in gaps in requirements being found later, leading to expensive fixes and slower delivery.
By incorporating AI into the process, EXANTE aims to catch gaps in requirements during the analysis stage, before tasks transition to the Ready for Testing column, thereby avoiding the creation of bugs. This integration allows testers to focus more on analysis and decision-making, leading to faster delivery and reduced time spent on routine tasks. However, the effectiveness of AI in this context varies, and there are limitations to its capabilities.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
