Predicting faults from cached history

By Sunghun Kim, Thomas Zimmermann, E. James Jr. Whitehead, Andreas Zeller. International Symposium on Electronic Commerce, 2008

478 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/89651cf2f67f456e3e1ac678bba15ffe1da683a6

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

An extensive comparison of bug prediction approaches (2010, background): “I. INTRODUCTION Defect prediction has generated widespread interest for a considerable period of time.”
How, and why, process metrics are better (2013, methodology): “[10]’s celebrated BugCache is populated using a simple co-commit history.”
Learning to rank relevant files for bug reports using domain knowledge (2014, background): “Kim et al. [21] cache fault-related code changes, and predict fault-prone entities based on the cached history.”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, background): “Many researchers mine bug report information to solve software engineering problems such as duplicate bug detection [33, 35], automatic bug triage [4, 7], bug report quality analysis [6, 7], and defect prediction [20, 37].”
Fair and balanced?: bias in bug-fix datasets (2009, unknown): “[27] along with methods used to gather data [46] used by BugCache.”
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].”
It's not a bug, it's a feature: How misclassification impacts bug prediction (2013, background): “Taking the blame, other typical examples with one of us as co-authors are: [5], [6], [7], [8], [9].”
On the "Naturalness" of Buggy Code (2015, background): “survey and provide a direct comparison of a number of representative DP approaches (including those using process metrics, such as previous changes [34, 17] and defects [27], and product metrics, such as code complexity metrics [6]).”
Software Defect Prediction via Convolutional Neural Network (2017, background): “Software defect prediction is an active research area in Software Engineering [3], [4], [5], [6], [7], [13], [27], [28].”
Dealing with noise in defect prediction (2011, background): “Defect prediction is a very active area in software engineering research [7, 10, 11, 13, 18, 19, 33, 35].”
Online Defect Prediction for Imbalanced Data (2015, background): “Change classi.cation predicts defects at the change level, where a change is the modi.cations to one .le in a commit.”
The promises and perils of mining git (2009, unknown): “A. E. Housman, A Shropshire Lad”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, unknown): “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].”