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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...
A large amount of risk evaluation formulas have been proposed for spectrum-based fault localization (SBFL) in prior studies. A recent study by Xie et al. developed an innovative framework to theoretically analyze the effectiveness of those risk evaluation formulas in SBFL. Xie et al.'s study was based on the assumption that program has only one fault. In other words, they investigated SBFL in the...
In order to identify vulnerable software components, developers can take software metrics as predictors or use text mining techniques to build vulnerability prediction models. A recent study reported that text mining based models have higher recall than software metrics based models. However, this conclusion was drawn without considering the sizes of individual components which affects the code inspection...
Software bug localization aiming to determine the locations needed to be fixed for a bug report is one of the most tedious and effort consuming activities in software debugging. Learning-to-rank (LR) is the state-of-the-art approach proposed by Ye et al. to recommending relevant files for bug localization. Ye et al.'s experimental results show that the LR approach significantly outperforms previous...
Background. Slice-based cohesion metrics leverage program slices with respect to the output variables of a module to quantify the strength of functional relatedness of the elements within the module. Although slice-based cohesion metrics have been proposed for many years, few empirical studies have been conducted to examine their actual usefulness in predicting fault-proneness. Objective. We aim to...
Previous studies have shown that process metrics are useful for building fault-proneness prediction models. In particular, it has been found that those process metrics incorporating developer experience (defined as the percentage of the code a developer contributes) exhibit a good ability to predict fault-proneness. However, developer quality, which we strongly believe should have a great influence...
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