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Background: An increasing research effort has devoted to just-in-time (JIT) defect prediction. A recent study by Yang et al. at FSE'16 leveraged individual change metrics to build unsupervised JIT defect prediction model. They found that many unsupervised models performed similarly to or better than the state-of-the-art supervised models in effort-aware JIT defect prediction. Goal: In Yang et al.'s...
When there exists not enough historical defect data for building accurate prediction model, semi-supervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data is well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only make use of the...
Software effort estimation (SEE) is a crucial step in software development. Effort data missing usually occurs in real-world data collection. Focusing on the missing data problem, existing SEE methods employ the deletion, ignoring, or imputation strategy to address the problem, where the imputation strategy was found to be more helpful for improving the estimation performance. Current imputation methods...
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