Stratifying large software files to improve prediction performance in software defect prediction-Reference-Cited by-同舟云学术

Stratifying large software files to improve prediction performance in software defect prediction

Published:2022-05-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 9th International Conference on Applied Computing & Information Technology
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Author:

Alshehri Yasser Ali¹^ORCID,Alnazzawi Noha¹^ORCID,Hijazi Haneen²^ORCID,Alharbi Rawan³^ORCID

Affiliation:

1. Computer Science and Engineering Dept, Yanbu Industrial College, Saudi Arabia

2. Dept of Software Engrg, Hashemite University, Jordan

3. Computer Science and Engineering Dept, Tabuk University, Saudi Arabia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3543895.3543924

Reference35 articles.

1. An empirical validation of object-oriented metrics in two different iterative software processes

2. Yasser Ali Alshehri. 2020. Can We Predict the Change in Code in a Software Product Line Project?Journal of Software Engineering and Applications 13 06(2020) 91. Yasser Ali Alshehri. 2020. Can We Predict the Change in Code in a Software Product Line Project?Journal of Software Engineering and Applications 13 06(2020) 91.

3. Yasser Ali Alshehri. 2020. Predicting change in newly created files in a software product line project. Software: Practice and Experience(2020). Yasser Ali Alshehri. 2020. Predicting change in newly created files in a software product line project. Software: Practice and Experience(2020).

4. Yasser Ali Alshehri , Katerina Goseva-Popstojanova , Dale G Dzielski , and Thomas Devine . 2018. Applying Machine Learning to Predict Software Fault Proneness Using Change Metrics , Static Code Metrics, and a Combination of Them. In SoutheastCon 2018 . IEEE , 1–7. Yasser Ali Alshehri, Katerina Goseva-Popstojanova, Dale G Dzielski, and Thomas Devine. 2018. Applying Machine Learning to Predict Software Fault Proneness Using Change Metrics, Static Code Metrics, and a Combination of Them. In SoutheastCon 2018. IEEE, 1–7.

5. Semi-Supervised Deep Fuzzy C-Mean Clustering for Software Fault Prediction