Relational databases are critical for many software systems, holding the most valuable data for organisations. Data engineers build relational databases using schemas to specify the structure of the data within a database and defining integrity constraints. These constraints protect the data's consistency and coherency, leading industry experts to recommend testing them.
Since manual schema testing is labour-intensive and error-prone, automated techniques enable the generation of test data. Although these generators are well-established and effective, they use default values and often produce many, long, and similar tests --- this results in decreasing fault detection and increasing regression testing time and testers inspection efforts. It raises the following questions: How effective is the optimised random generator at generating tests and its fault detection compared to prior methods? What factors make tests understandable for testers? How to reduce tests while maintaining effectiveness? How effectively do testers inspect differently reduced tests?
To answer these questions, the first contribution of this thesis is to evaluate a new optimised random generator against well-established methods empirically. Secondly, identifying understandability factors of schema tests using a human study. Thirdly, evaluating a novel approach that reduces and merge tests against traditional reduction methods. Finally, studying testers' inspection efforts with differently reduced tests using a human study.
The results show that the optimised random method efficiently generates effective tests compared to well-established methods. Testers reported that many NULLs and negative numbers are confusing, and they prefer simple repetition of unimportant values and readable strings. The reduction technique with merging is the most effective at minimising tests and producing efficient tests while maintaining effectiveness compared to traditional methods. The merged tests showed an increase in inspection efficiency with a slight accuracy decrease compared to only reduced tests. Therefore, these techniques and investigations can help practitioners adopt these generators in practice
