TL;DR
This paper explores how Formal Concept Analysis (FCA) can improve software testing by enhancing test case derivation and supporting regression testing analysis through an FCA-based machine learning system.
Contribution
It introduces the application of FCA tools to automate and improve various aspects of software testing processes.
Findings
FCA improves the efficiency of test case derivation.
FCA-based machine learning supports regression testing analysis.
The approach enhances software testing effectiveness.
Abstract
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.
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Taxonomy
TopicsRough Sets and Fuzzy Logic · Data Mining Algorithms and Applications · Semantic Web and Ontologies