Application of Hybrid Search Based Algorithms for Software Defect Prediction

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Wasiur Rhmann 1,*

1. Department of Computer Science and Information Technology, Babasaheb Bhimrao Ambedkar University, (A Central University), Satellite Campus, Amethi, U.P., India

* Corresponding author.


Received: 20 Nov. 2017 / Revised: 26 Dec. 2017 / Accepted: 15 Jan. 2018 / Published: 8 Apr. 2018

Index Terms

Defect, Static metrics, Cyclomatic complexity, Halstead metrics


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.

Cite This Paper

Wasiur Rhmann, " Application of Hybrid Search Based Algorithms for Software Defect Prediction", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.4, pp. 51-62, 2018. DOI:10.5815/ijmecs.2018.04.07


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