The replication toolkit for the paper "Don’t worry, we’ll get there: developing defect prediction models insensitive to snoring noise"
Titile: Don’t worry, we’ll get there: developing defect prediction models insensitive to snoring noise
Our work aim to explore easy-to-use modeling techniques, instead of the training data refinement approach [1], to mitigate the negative influence of snoring noise [2] in a training set. We propose simple modeling techniques to build five snoring-noise-insensitive defect prediction models: LOC, LOC-N, FEATURE, FEATURE-N, and FEATURE-LOC. In view of the effectiveness and simplicity of these models, we suggest using them as easy-to-implement baselines in future studies to demonstrate the usefulness of any newly proposed snoring-noise mitigation approaches.
(1) /DataSets/ Our defect data sets consists of two groups, the first one is the open-source defect data sets from the literature [3], and the second one is our newly collected defect data set from 8 Apache projects. Please refer to the /DataSets/README.md for details.
(2) /Scripts_CollectingFeaturesAndLabels/ In this folder, the folder Code_CollectFeatures holds the feature collection scripts we implemented and the folder Code_CollectLabels holds the defect label collection scripts we implemented. Please refer to the /Scripts_CollectingFeaturesAndLabels/README.md for details.
(3) /SnoringNoiseProgram/ This folder holds the experimental programs for reproducing the experimental results in RQ1, RQ2 and Discussions. Please refer to the /SnoringNoiseProgram/README.md to learn how to run the JAR program.
If you use the data set (our newly collected defect data sets for 8 Apache projects) or the program code, please cite our paper "Don’t worry, we’ll get there: developing defect prediction models insensitive to snoring noise", thanks.
[1] D. Falessi, A. Ahluwalia, M.D. Penta. The impact of dormant defects on defect prediction: a study of 19 apache projects. ACM Transactions on Software Engineering and Methodology, 31 (4), 2022: 1–26.
[2] A. Ahluwalia, D. Falessi, M.D. Penta. Snoring: A noise in defect prediction datasets. MSR 2019: 63-67.
[3] D.A. Costa, S. McIntosh, W. Shang, U. Kulesza, R. Coelho, A.E. Hassan. A framework for evaluating the results of the SZZ approach for identifying bug-introducing changes. IEEE Transactions on Software Engineering, 43(7), 2017: 641-657.