Predicting Defects for Eclipse

By Thomas Zimmermann, Rahul Premraj, Andreas Zeller. International Conference on Predictive Models in Software Engineering, 2007

796 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/a325f1b0694ec557011ac5994fc50a9d99d01592

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Citations in the corpus

On the "Naturalness" of Buggy Code (2015, methodology): “DP is typically used at release-time to predict post-release bugs [35, 57, 42, 36, 11]; so, for this comparison we use the post release bug data collected in Phase-II.”
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (2015, methodology): “For example, in early studies, researchers used simple techniques like logistic regression [2, 7, 47] and linear regression [24, 65] to train defect prediction models.”
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, result): “Such unrealistic and unstable performance estimates could lead to incorrect model selection in practice and inaccurate conclusions for defect prediction studies [70], [105].”
Software fault prediction metrics: A systematic literature review (2013, methodology): “Static code metrics, like complexity, size and OO metrics, are useful for observing particular software version, but their prediction accuracy decreases with each software iteration [128,136,57,156,89].”
Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016, methodology): “We use the Scott-Knott ESD test in order to control for dataset-specific model performance, since some datasets may have a tendency to produce over- or under-performing classifiers.”
It's not a bug, it's a feature: How misclassification impacts bug prediction (2013, methodology): “[2], a mapping method frequently replicated by many studies.”
ReLink: recovering links between bugs and changes (2011, methodology): “Defect information is also used to train models for defect prediction [17, 18, 33, 35, 36].”
Fair and balanced?: bias in bug-fix datasets (2009, positive): “Since we have two data sets for Eclipse, we refer to the publicly available data set from Zimmermann et al. [49] as EclipseZ and the data we mined [3] to reduce false negative links as EclipseB.”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, neutral): “First, defect prediction models can be used to predict modules that are likely to be defect-prone in the future [1], [2], [3], [4], [5], [6], [7], [8], [9].”
How, and why, process metrics are better (2013, background): “The value of code metrics in defect prediction has been well-explored [12, 16, 29].”
The impact of tangled code changes (2013, background): “As the change set contains a bug fix (task A) and may even state so in the commit message, the tangled commit may be classified as a bug fix [2].”
Automatically Learning Semantic Features for Defect Prediction (2016, methodology): “Software defect prediction techniques [12,18,20,23,27,32, 38, 40, 52, 62, 70] have been proposed to detect defects and reduce software development costs.”
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process (2009, neutral): “[35] T. Zimmermann, R. Premraj, and A. Zeller, "Predicting Defects for Eclipse," in Third International Workshop on Predictor Models in Software Engineering 2007.”
An extensive comparison of bug prediction approaches (2010, methodology): “We minimized this threat by using parts of Eclipse in our benchmark, a system that while being open-source has a strong industrial background.”