An empirical study on dependence clusters for effort-aware fault-proneness prediction

Author:

Yang Yibiao1,Harman Mark2,Krinke Jens2,Islam Syed3,Binkley David4,Zhou Yuming1,Xu Baowen1

Affiliation:

1. Nanjing University, China

2. University College London, UK

3. University of East London, UK

4. Loyola University Maryland, USA

Funder

program A for Outstanding PhD candidate of Nanjing University

National Key Basic Research and Development Program of China

J. William Fulbright award

EPSRC DAASE programme grant

National Natural Science Foundation of China

NSF grant IIA-1360707

Natural Science Foundation of Jiangsu Province

Publisher

ACM

Cited by 28 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bug numbers matter: An empirical study of effort‐aware defect prediction using class labels versus bug numbers;Software: Practice and Experience;2024-07-10

2. Risky Dynamic Typing-related Practices in Python: An Empirical Study;ACM Transactions on Software Engineering and Methodology;2024-06-27

3. Machine Learning-based Models for Predicting Defective Packages;2024 The 8th International Conference on Machine Learning and Soft Computing;2024-01-26

4. Investigating the Impact of Bug Dependencies on Bug-Fixing Time Prediction;2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM);2023-10-26

5. The Impact of the bug number on Effort-Aware Defect Prediction: An Empirical Study;Proceedings of the 14th Asia-Pacific Symposium on Internetware;2023-08-04

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