Test data plays a central role in modern software development. It enables data errors to be identified at an early stage and quality assurance to be optimized. The use of AI and low-code solutions makes test automation more efficient. Close cooperation between IT and the specialist department is crucial in order to harmonize data processes and increase quality.
In this episode, I talked to Joshua and Hermann about quality, test automation and agility. Hermann explained how important it is to detect data errors early on and how to systematically generate target results in order to compare them with actual results. He emphasized that there is often a lack of suitable tools to carry out these tests efficiently. Joshua added that their methods help companies to harmonize and test data from different systems. We also talked about the role of artificial intelligence in the testing process and how it can help make suggestions for testing and improve collaboration between IT and business departments. Finally, there were insights into the challenges and benefits of visualizing data processes to optimize quality assurance.
“The problem is that you have data errors. They often appear far too late. In other words, you actually want to test data during development.” Hermann Friebel
Hermann Friebel, founder and Managing Director of FINARIS Financial Software Development GmbH since 2001, has almost four decades of expertise in software development and testing in the areas of securities trading and risk controlling.
Since joining FINARIS in 2015, Joshua Claßen has established himself as a senior consultant for backend test automation of complex banking applications. Through his work with RapidRep, the predecessor of SQACE, he gained extensive experience in automated data testing and data quality assurance with various clients.
In many companies, data errors are often only recognized at a late stage. Although the IT department usually has technical access to the data, it often lacks the necessary business expertise to identify data errors at an early stage. In most cases, the specialist department develops specific test cases and checks them, but this is often cumbersome and inefficient.
A promising method for solving this problem is to systematically generate target results and compare them with the actual results. However, this technique often only covers about 70% of the relevant cases. To ensure complete coverage, the remaining 30% should be supplemented by generated test data. In this way, almost comprehensive coverage of all possible scenarios can be achieved.
Close cooperation between IT and the specialist department is essential in order to improve the detection of data errors. By using special tools, employees from the specialist department can actively work on the data processes and support their further development. This collaboration not only promotes the quality of the test results, but also mutual understanding between IT and specialist departments, which leads to better data processes in the long term.
The use of low-code components helps to simplify the testing process. With these tools, many tasks can be implemented more quickly and without in-depth technical knowledge. Especially in large companies with many departments in which similar problems often occur, low-code components help to avoid redundant solutions and make processes more efficient.
Artificial intelligence (AI) has the potential to revolutionize test automation. AI can help to automatically generate suggestions for tests or create code, making the process more efficient and saving valuable time. However, it is important to remain realistic: AI-supported systems make work easier and optimize processes, but they do not take on all tasks completely autonomously.