Heterogeneous Defect Prediction

By Jaechang Nam, Wei Fu, Sunghun Kim, Tim Menzies, Lin Tan. IEEE Transactions on Software Engineering, 2015

397 citations recorded by the source corpus.

Many recent studies have documented the success of cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. However, most studies share the same limitations: it requires homogeneous data; i.e., different projects must describe themselves using the same metrics. This paper presents methods for heterogeneous defect prediction (HDP) that matches up different metrics in different projects. Metric matching for HDP requires a “large enough” sample of distributions in the source and target projects—which raises the question on how large is “large enough” for effective heterogeneous defect prediction. This paper shows that empirically and theoretically, “large enough” may be very small indeed. For example, using a mathematical model of defect prediction, we identify categories of data sets were as few as 50 instances are enough to build a defect prediction model. Our conclusion for this work is that, even when projects use different metric sets, it is possible to quickly transfer lessons learned about defect prediction.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/7d50e31d3cb37c4f095465f92d2301750144eec2

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Citations in the corpus

Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks (2019, background): “, text sentiment classification [10], image analysis and classification [8], [9], [15], human activity detection [10], software defect identification [11] and multi-lan-”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, background): “, cross-project defect prediction [89], just-in-time defect prediction [90], heterogenous defect prediction [91]).”
Improving bug detection via context-based code representation learning and attention-based neural networks (2019, methodology): “While the ML-based bug detection models [Nam and Kim 2015] rely on feature selections, the deep learning-based ones [Pradel and Sen 2018; Wang et al. 2016b] take advantages of the capability to learn the important features from the training data for bug detection.”