Applying FCA toolbox to Software Testing
This work addresses software testing challenges for developers and testers, but it appears incremental as it applies an existing FCA toolbox to this domain.
The paper tackles the problem of improving software testing efficiency by applying Formal Concept Analysis (FCA) to derive test cases and analyze regression testing results, resulting in considerable improvements in test case derivation.
Software testing uses wide range of different tools to enhance the complicated process of defining quality of the system under test. Formal Concept Analysis (FCA) provides us with algorithms of deriving formal ontology from a set of objects and their attributes. With the use of FCA we can considerably improve the efficiency of test case derivation. Moreover, an FCA-based machine learning system supports the analysis of regression testing results.