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Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models
TLDR
The results suggest that some classification techniques tend to produce defect prediction models that outperform others, contrary to earlier research.
View on ACM
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
TLDR
It is found that single-repetition holdout validation tends to produce estimates with 46-229 percent more bias and 53-863 percent more variance than the top-ranked model validation techniques, and out-of-sample bootstrap validation yields the best balance between the bias and variance.
View on IEEE
Automated Parameter Optimization of Classification Techniques for Defect Prediction Models
TLDR
This paper concludes that parameter settings can indeed have a large impact on the performance of defect prediction models, suggesting that researchers should experiment with the parameters of the classification techniques.
View on ACM
Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction
TLDR
It is found that fluctuations in the properties of fix-inducing changes can impact the performance and interpretation of JIT models and quality improvement plans should be informed by JIT model that are trained using six months (or more) of historical data.
View on IEEE
The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects
TLDR
The results empirically confirm the intuition that poorly reviewed code has a negative impact on software quality in large systems using modern reviewing tools.
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A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes
TLDR
The proposed framework provides a systematic mean for evaluating the data that is generated by a given SZZ implementation and finds that current SZZ implementations still lack mechanisms to accurately identify bug-introducing changes.
View on IEEE
Studying just-in-time defect prediction using cross-project models
TLDR
An empirical study on 11 open source projects finds that while JIT models rarely perform well in a cross-project context, their performance tends to improve when using approaches that select models trained using other projects that are similar to the testing project, and combine the models of several other projects to produce an ensemble model.
View on Springer
An empirical study of the impact of modern code review practices on software quality
TLDR
Through a case study of the Qt, VTK, and ITK projects, it is found that code review coverage, participation, and expertise share a significant link with software quality.
View on Springer
Analyzing the State of Static Analysis: A Large-Scale Evaluation in Open Source Software
TLDR
The results show that ASAT use is widespread, but not ubiquitous, and that projects typically do not enforce a strict policy on AsAT use.
View on IEEE
An empirical study of just-in-time defect prediction using cross-project models
TLDR
JIT cross-project models learned using other projects are a viable solution for projects with little historical data, but perform best when the data used to learn them is carefully selected.
View on ACM
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