Localização evolucionária de defeitos em software baseada na singularidade de escores de suspeita
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2022-10-13
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Universidade Federal de Goiás
Resumo
Context. Software is subject to the presence of faults, which impacts its quality as well as
production and maintenance costs. Evolutionary fault localization has used data from the test
activity (test spectra) as a source of information about defects, and its automation aims to
obtain better accuracy and lower software repair cost.
Motivation. Our analysis identified that test spectra commonly used in the research field have
a high ratio of sample repetition, which impairs the training and evolution of models
(heuristics).
Problem. We investigate whether the uniqueness of suspiciousness scores can boost the
ability to find software faults, aiming to deal with samples repetition, that is, if an exploration
based on how distinguishable program elements are about being defective can generate
competitive models.
Methodology. The investigation formalized hypotheses, introduced three training strategies
to guide the proposal and carried out an experimental evaluation, aiming to reach conclusions
regarding the assessment of research questions and hypotheses.
Analysis. The results have shown the competitiveness of all the proposed training strategies
through evaluation metrics commonly used in the research field.
Conclusion. Statistical analyses confirmed that the uniqueness of suspiciousness scores
guides the generation of superior heuristics for fault localization.
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FERREIRA, Willian de Jesus. Localização evolucionária de defeitos em software baseada na singularidade de escores de suspeita. 2022. 79 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2022.