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In the last decades, the research community has devoted a lot of effort in the definition of approaches able to predict the defect proneness of source code files. Such approaches exploit several predictors (e.g., product or process metrics) and use machine learning classifiers to predict classes into buggy or not buggy, or provide the likelihood that a class will exhibit a fault in the near future...
Code smells are symptoms of poor design and implementation choices. Previous studies empirically assessed the impact of smells on code quality and clearly indicate their negative impact on maintainability, including a higher bug-proneness of components affected by code smells. In this paper we capture previous findings on bug-proneness to build a specialized bug prediction model for smelly classes...
The importance of human-related factors in the introduction of bugs has recently been the subject of a number of empirical studies. However, such factors have not been captured yet in bug prediction models which simply exploit product metrics or process metrics based on the number and type of changes or on the number of developers working on a software component. Previous studies have demonstrated...
Existing defect prediction models use product or process metrics and machine learning methods to identify defect-prone source code entities. Different classifiers (e.g., linear regression, logistic regression, or classification trees) have been investigated in the last decade. The results achieved so far are sometimes contrasting and do not show a clear winner. In this paper we present an empirical...
Cross-project defect prediction is very appealing because (i) it allows predicting defects in projects for which the availability of data is limited, and (ii) it allows producing generalizable prediction models. However, existing research suggests that cross-project prediction is particularly challenging and, due to heterogeneity of projects, prediction accuracy is not always very good. This paper...
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