In today’s time, software quality assurance is the most essential and costly set of activities during software development in the information technology (IT) industries. Finding defects in system modules has always been one of the most relevant problems in software engineering, leading to increased costs and reduced confidence in the product, resulting in dissatisfaction with customer requirements. Therefore, to provide and deliver an efficient software product with as few defects as possible on time and of good quality, it is necessary to use machine learning techniques and models, such as supervised learning to accurately classify and predict defects in each of the software development life cycle (SDLC) phases before delivering a software product to the customer. The main objective is to evaluate the performance of different machine learning models in software defect prediction applied to 4 NASA datasets, such as CM1, JM1, KC1, and PC1, then de-terminate and select the best performing model using the MCDM: VIKOR multi-criteria decision-making method.
|Title of host publication||Human Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET|
|Subtitle of host publication||Future Systems IHIET-FS 2021|
|Editors||Tareq Ahram, Redha Taiar|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||9|
|State||Published - 2022|
|Event||5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021 - Virtual, Online|
Duration: 27 Aug 2021 → 29 Aug 2021
|Name||Lecture Notes in Networks and Systems|
|Conference||5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021|
|Period||27/08/21 → 29/08/21|
Bibliographical noteFunding Information:
Acknowledgments. This work has been supported by the GIIAR research group and the Universidad Politécnica Salesiana.
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Machine learning
- NASA dataset
- Software defect prediction
- VIKOR method