Article
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Comparative Analysis of Software Defect PredictionTechniques
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: Received: 4 December 2019 / Approved: 6 December 2019 / Online: 6 December 2019 (04:25:21 CET)
How to cite: Sirshar, M.; Mir, H.; Amir, K.; Zainab, L. Comparative Analysis of Software Defect PredictionTechniques. Preprints 2019, 2019120075 Sirshar, M.; Mir, H.; Amir, K.; Zainab, L. Comparative Analysis of Software Defect PredictionTechniques. Preprints 2019, 2019120075
Abstract
Accurate prediction of defects in software components plays a vital role in administrating the quality of the quality and efficiency of the system to be developed. So we have written a systematic literature review in order to evaluate the four main defect prediction techniques. Defect prediction paves way for the testers to find bugs and modify them in order to achieve input to output conformance. In this paper we have discussed the open issues in predicting software defects and have provided with a detailed analyzation of different methods including Machine Learning, Integrated Approach, Cross-Project and Deep Forest algorithm in order to prevent these flaws. However, it is almost impossible to rule which method is better than the other so every technique can be analyzed separately and the best technique according to the problem at hand can be used or can be altered to create hybrid technique suitable for the cause.
Keywords
software defect prediction; machine learning approach; integrated approach; Deep Forest
Subject
Engineering, Automotive Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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