Application of hybrid search based algorithms for software defect prediction

Application of hybrid search based algorithms for software defect prediction

Автор: Wasiur Rhmann

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 4 vol.10, 2018 года.

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In software engineering software defect class prediction can help to take decision for proper allocation of resources in software testing phase. Identification of highly defect prone classes will get more attention from tester as well as security experts. In recent years various artificial techniques are used by researchers in different phases of SDLC. Main objective of the study is to compare the performances of Hybrid Search Based Algorithms in prediction of defect proneness of a class in software. Statistical test are used to compare the performances of developed prediction models, Validation of the models is performed with the different releases of datasets.

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Defect, Static metrics, Cyclomatic complexity, Halstead metrics

Короткий адрес: https://sciup.org/15016756

IDR: 15016756   |   DOI: 10.5815/ijmecs.2018.04.07

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