Just-In-Time Software Defect Prediction (JIT-SDP)

2023

1. Deep Incremental Learning of Imbalanced Data for Just-In-Time Software Defect Prediction

   Yunhua Zhao and Hui Chen

   CoRR, 2023

   DOI Bib

   @article{DBLP:journals/corr/abs-2310-12289,
     author = {Zhao, Yunhua and Chen, Hui},
     title = {Deep Incremental Learning of Imbalanced Data for Just-In-Time Software Defect Prediction},
     journal = {CoRR},
     volume = {abs/2310.12289},
     year = {2023},
     url = {https://doi.org/10.48550/arXiv.2310.12289},
     doi = {10.48550/ARXIV.2310.12289},
     eprinttype = {arXiv},
     eprint = {2310.12289},
     timestamp = {Fri, 27 Oct 2023 12:21:19 +0200},
     biburl = {https://dblp.org/rec/journals/corr/abs-2310-12289.bib},
     bibsource = {dblp computer science bibliography, https://dblp.org}
   }

2. A Systematic Survey of Just-in-Time Software Defect Prediction

   Yunhua Zhao, Kostadin Damevski, and Hui Chen

   ACM Computing Surveys, Feb 2023

   Abs DOI Bib

   Recent years have experienced sustained focus in research on software defect prediction that aims to predict the likelihood of software defects. Moreover, with the increased interest in continuous deployment, a variant of software defect prediction called Just-in-Time Software Defect Prediction (JIT-SDP) focuses on predicting whether each incremental software change is defective. JIT-SDP is unique in that it consists of two interconnected data streams, one consisting of the arrivals of software changes stemming from design and implementation, and the other the (defective or clean) labels of software changes resulting from quality assurance processes.We present a systematic survey of 67 JIT-SDP studies with the objective to help researchers advance the state of the art in JIT-SDP and to help practitioners become familiar with recent progress. We summarize best practices in each phase of the JIT-SDP workflow, carry out a meta-analysis of prior studies, and suggest future research directions. Our meta-analysis of JIT-SDP studies indicates, among other findings, that the predictive performance correlates with change defect ratio, suggesting that JIT-SDP is most performant in projects that experience relatively high defect ratios. Future research directions for JIT-SDP include situating each technique into its application domain, reliability-aware JIT-SDP, and user-centered JIT-SDP.

   @article{zhao2022systematic,
     author = {Zhao, Yunhua and Damevski, Kostadin and Chen, Hui},
     title = {A Systematic Survey of Just-in-Time Software Defect Prediction},
     month = feb,
     year = {2023},
     issue_date = {October 2023},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {55},
     number = {10},
     pages = {201:1-201:35},
     issn = {0360-0300},
     url = {https://dl.acm.org/doi/10.1145/3567550},
     doi = {10.1145/3567550},
     journal = {ACM Computing Surveys},
     articleno = {201},
     numpages = {35},
     keywords = {software change metrics, release software defect prediction, machine learning, searching-based algorithms, Software defect prediction, just-in-time software defect prediction, change defect density, change-level software defect prediction}
   }

3. Supplementary Material for: A Systematic Survey of Just-in-time Software Defect Prediction

   Yunhua Zhao, Kostadin Damevski, and Hui Chen

   ACM Computing Surveys, Feb 2023

   Online only: \urlhttps://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3567550&file=3567550-supp.pdf

   Bib

   @article{zhao2022systematicsuppl,
     author = {Zhao, Yunhua and Damevski, Kostadin and Chen, Hui},
     title = {Supplementary Material for:
     		A Systematic Survey of Just-in-time Software Defect
     			Prediction},
     year = {2023},
     issue_date = {October 2023},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {55},
     number = {10},
     pages = {201:1-201:25},
     issn = {0360-0300},
     url = {https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3567550&file=3567550-supp.pdf},
     note = {Online only: \url{https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F3567550&file=3567550-supp.pdf}},
     journal = {ACM Computing Surveys},
     month = feb,
     numpages = {25}
   }