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A large-scale empirical study of just-in-time quality assurance
TLDR
The findings indicate that “Just-In-Time Quality Assurance” may provide an effort-reducing way to focus on the most risky changes and thus reduce the costs of developing high-quality software.
View on IEEE
What are developers talking about? An analysis of topics and trends in Stack Overflow
TLDR
This article uses latent Dirichlet allocation (LDA), a statistical topic modeling technique, to automatically discover the main topics present in developer discussions of Stack Overflow and analyzes these discovered topics, as well as their relationships and trends over time, to gain insights into the development community.
View on Springer
Predicting faults using the complexity of code changes
  • A. Hassan
  • Computer Science
    IEEE 31st International Conference on Software…
  • 16 May 2009
TLDR
A case study shows that the change complexity metrics proposed are better predictors of fault potential in comparison to other well-known historical predictor of faults, i.e., prior modifications and prior faults.
View on ACM
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
Predicting change propagation in software systems
  • A. HassanR. Holt
  • Computer Science
    20th IEEE International Conference on Software…
  • 11 September 2004
TLDR
This paper proposes several heuristics to predict change propagation of source code entities, and presents a framework to measure the performance of the proposed heuristic performance.
View on IEEE
Cross-Project Defect Prediction Using a Connectivity-Based Unsupervised Classifier
TLDR
In the cross-project setting, the proposed connectivity-based unsupervised classifier (via spectral clustering) ranks as one of the top classifiers among five widely-used supervised classifiers and five un supervised classifiers (i.e., random forest, naive Bayes, logistic regression, decision tree, and logistic model tree), while only random forest ranks in the first tier.
View on ACM
Clustering WSDL Documents to Bootstrap the Discovery of Web Services
TLDR
This paper proposes a novel technique to mine Web Service Description Language (WSDL) documents and cluster them into functionally similar Web service groups, as a predecessor step to retrieving the relevant Web services for a user request by search engines.
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.
View on ACM
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