Amalgamation of Automated Testing and Data Mining : A Novel Approach in Software Testing
Sarita Kulbhushan Sharma, Anamika Shukla Sharma · 2011
Software engineering comprehends several disciplines devoted to prevent and remedy malfunctions and to warrant adequate behavior. Testing is a widespread validation approach in industry, but it is still largely ad hoc, expensive, and unpredictably effective. In today's industry, the design of software tests is mostly based on the testers' expertise, while test automation tools are limited to execution of pre-planned tests only. Evaluation of test outputs is also associated with a considerable effort by human testers who often have improper knowledge of the requirements specification. This manual approach to software testing results in heavy losses to the world's economy. This paper proposes the potential use of data mining algorithms for automated induction of functional requirements from execution data. The induced data mining models of tested software can be utilized for recovering missing and incomplete specifications, designing a minimal set of regression tests, and evaluating the correctness of software outputs when testing new, potentially inconsistent releases of the system.