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BAYESIAN-LEARNING BASED GUIDELINES TO DETERMINE EQUIVALENT MUTANTS

VINCENZI, AURI MARCELO RIZZO ; NAKAGAWA, ELISA YUMI ; MALDONADO, JOSÉ CARLOS ; DELAMARO, MÁRCIO EDUARDO ; ROMERO, ROSELI APARECIDA FRANCELIN

International journal of software engineering and knowledge engineering, 2002-12, Vol.12 (6), p.675-689 [Periódico revisado por pares]

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Título:
BAYESIAN-LEARNING BASED GUIDELINES TO DETERMINE EQUIVALENT MUTANTS
Autor: VINCENZI, AURI MARCELO RIZZO ; NAKAGAWA, ELISA YUMI ; MALDONADO, JOSÉ CARLOS ; DELAMARO, MÁRCIO EDUARDO ; ROMERO, ROSELI APARECIDA FRANCELIN
É parte de: International journal of software engineering and knowledge engineering, 2002-12, Vol.12 (6), p.675-689
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: Mutation testing (Mutation Analysis), although powerful in revealing faults, is considered a computationally expensive criterion, due to the high number of mutants created and the effort to determine the equivalent mutants. Using mutation-based alternative testing criteria it is possible to reduce the number of mutants but it is still necessary to determine the equivalent ones. In this paper the Bayesian Learning(one of the Artificial Intelligence techniques used in machine learning) is investigated to define the Bayesian Learning-Based Equivalent Detection Technique (BaLBEDeT), which provides guidelines to help the tester to analyze the live mutants in order to determine the equivalent ones.
Editor: World Scientific Publishing Company
Idioma: Inglês

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