Entropy-Based Test Generation for Improved Fault Localization

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2013

Autores

José Campos (Autor) FEUP; Rui Abreu (Autor) FEUP; Gordon Fraser (Autor) Outra; Marcelo d'Amorim (Autor) Outra

Ata de Conferência Internacional

Título: 2013 28TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE)

Páginas: 257-267

28th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Palo Alto, CA, NOV 11-15, 2013

Indexação

ISI Proceedings

ISI Web of Knowledge - 35 Citações

ISI Web of Science

Scopus - 50 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-008-YED

DOI: 10.1109/ase.2013.6693085

Abstract (EN): Spectrum-based Bayesian reasoning can effectively rank candidate fault locations based on passing/failing test cases, but the diagnostic quality highly depends on the size and diversity of the underlying test suite. As test suites in practice often do not exhibit the necessary properties, we present a technique to extend existing test suites with new test cases that optimize the diagnostic quality. We apply probability theory concepts to guide test case generation using entropy, such that the amount of uncertainty in the diagnostic ranking is minimized. Our ENTBUG prototype extends the search-based test generation tool EVOSUITE to use entropy in the fitness function of its underlying genetic algorithm, and we applied it to seven real faults. Empirical results show that our approach reduces the entropy of the diagnostic ranking by 49% on average (compared to using the original test suite), leading to a 91% average reduction of diagnosis candidates needed to inspect to find the true faulty one.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11