ReLink: recovering links between bugs and changes

By Rongxin Wu, Hongyu Zhang, Sunghun Kim, Shing-Chi Cheung. ESEC/FSE '11, 2011

423 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/c88900f11d93df3b02448686e74619349e3d3ab4

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Citations in the corpus

An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, background): “The conclusions that are derived from defect prediction models may not be sound if the estimated performance is unrealistic or unstable.”
Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016, background): “[26, 61], 5 are provided by D’Ambros et al.”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “[29], 5 datasets are provided by D’Ambros et al.”
Transfer defect learning (2013, methodology): “The following are used to define precision, recall, and Fmeasure: (i) predicting a buggy instance as buggy (b → b); (ii) predicting a buggy instance as clean (b → c); and (iii) predicting a clean instance as buggy (c → b).”
The Impact of Automated Parameter Optimization on Defect Prediction Models (2018, methodology): “For example, concerns about the quality of defect datasets have been raised [13], [102], [114], [137], [148].”
It's not a bug, it's a feature: How misclassification impacts bug prediction (2013, background): “[32] developed an automatic link recovery algorithm that learn bug report and code change criteria to recover missing links between bug reports and code changes.”
Heterogeneous Defect Prediction (2015, methodology): “[71] to improve the defect prediction performance by increasing the quality of the defect data and have 26 code complexity metrics extracted by the Understand tool [74].”
A large-scale empirical study of just-in-time quality assurance (2013, methodology): “We believe that the usage of an approach to recover missing links [59] is required to improve the accuracy of the SZZ algorithm to identify defect-inducing changes from repositories.”
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017, neutral): “[86], which considers the following constraints: (i) matching the committer/authors with issue tracking contributor name/email; (ii) the time interval between the commit and the last comment posted by the same author/contributor on the issue tracker must be less than seven days; and (iii) the Vector”