Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models-Reference-Cited by-同舟云学术

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Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models

Published:2016-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering
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Author:

Yang Yibiao¹,Zhou Yuming¹,Liu Jinping¹,Zhao Yangyang¹,Lu Hongmin¹,Xu Lei¹,Xu Baowen¹,Leung Hareton²

Affiliation:

1. Nanjing University, China

2. Hong Kong Polytechnic University, China

Funder

program A for Outstanding PhD candidate of Nanjing University.

Natural Science Foundation of Jiangsu Province

PolyU Grant

National Natural Science Foundation of China

Hong Kong Competitive Earmarked Research Grant

National Key Basic Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2950290.2950353

Reference45 articles.

1. A systematic and comprehensive investigation of methods to build and evaluate fault prediction models

2. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

3. Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm

4. An extensive comparison of bug prediction approaches

5. Time variance and defect prediction in software projects

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3. Just-in-Time Software Defect Prediction Techniques: A Survey;2024 15th International Conference on Information and Communication Systems (ICICS);2024-08-13

4. Effort-Aware Fault-Proneness Prediction Using Non-API-Based Package-Modularization Metrics;Mathematics;2024-07-13

5. Mining Action Rules for Defect Reduction Planning;Proceedings of the ACM on Software Engineering;2024-07-12

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