Populating a Release History Database from version control and bug tracking systems

By M. Fischer, Martin Pinzger, Harald C. Gall. 2003

593 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Software Engineering

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Citations in the corpus

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation (2018, background): “he Google BigQuery APIs to identify all commits having a message ACM Trans. Softw. Eng. Methodol., Vol. -, No. -, Article -. Publication date: February 2019. -:4 Tufano et al. containing the patterns [17]: (“fix” or “solve”) and (“bug” or “issue” or “problem” or “error”). We identified∼10M (10,056,0”
When do changes induce fixes? (2005, methodology): “In our approach, we refine the techniques of Čubranić and Murphy [4] and of Fischer, Pinzger, and Gall [6, 5], who identified references to bug databases in log messages and used these references to infer links from CVS archives to BUGZILLA databases.”
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017, unknown): “[25] matching the issue ID in the commit note.”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, unknown): “While this is certainly not a satisfactory situation, nonetheless this technique represents the state of the art in linking bugs to versioning system files ( Fischer et al. 2003 ; Z immermann et al.2007).”
Predicting Defects for Eclipse (2007, unknown): “In recent years, a number of techniques have been developed to relate bug reports to fixes [3] [6] [17].”
Fair and balanced?: bias in bug-fix datasets (2009, unknown): “Figure 1 depicts a snapshot of the various sources of data that have been used for both hypothesis testing and bug prediction in the past [19].”
An extensive comparison of bug prediction approaches (2010, methodology): “However, this technique represents the state of the art in linking bugs to versioning system files [18], [24].”
The road ahead for Mining Software Repositories (2008, methodology): “Many heuristics are used to extract data from code repositories (e.g., [24, 67, 18 , 49]), from email repositories (e.g., [8, 56]), and from run-time repositories (e.g., [31]).”
Mining metrics to predict component failures (2006, background): “The ke y idea is that one can map problems (in the bug database) to fixes (in the version database) and thus to those locations in th e code that caused the problem [9, 12, 20].”
UMLDiff: an algorithm for object-oriented design differencing (2005, methodology): “Consistently maintained change documentation [5,6], if it exists, is a reliable source of information as to what has been changed and what is the rationale behind the change.”
It's not a bug, it's a feature: How misclassification impacts bug prediction (2013, background): “[3] and Cubranic and Murphy [27] were among the first that introduced heuristics that allow mapping bug reports to applied code changes.”
ReLink: recovering links between bugs and changes (2011, background): “To improve the correctness of identified links between defects and changes, Fisher et al. [13, 14] discussed the confidence of links.”
Predicting source code changes by mining change history (2004, background): “Having extracted the change patterns in the first two stages, we do not need to regenerate the change patterns each time we query for a recommendation.”
Is it a bug or an enhancement?: a text-based approach to classify change requests (2008, methodology): “[8] discussed their experience in populating a relational database with data from Mozilla CVS and bug report repositories.”