Predicting defects using network analysis on dependency graphs

By Thomas Zimmermann, N. Nagappan. 2008 ACM/IEEE 30th International Conference on Software Engineering, 2008

591 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/8cd6e0c4f65ccc7f48b4f63748ca9fd134322eec

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Citations in the corpus

Deep Learning for Just-in-Time Defect Prediction (2015, background): “use network analysis to analyze dependencies between various pieces of code, which can help managers to identify central program units that are more likely to be defective [5].”
Transfer defect learning (2013, methodology): “Moreover, the F-measure of ZXing⇒Safe with TCA (0.70) is greater than that with TCA+ (0.64).”
Software fault prediction metrics: A systematic literature review (2013, background): “From the evidence gathered, it seems complexity metrics are not bad fault predictors, but others are better [112,84,88,154,155].”
Heterogeneous Defect Prediction (2015, background): “Software defect prediction is one of the most active research areas in software engineering [8, 9, 24, 25, 26, 36, 37, 43, 47, 58, 59].”
An extensive comparison of bug prediction approaches (2010, methodology): “Zimmermann and Nagappan used dependencies between binaries in Windows server 2003 to predict defect [19].”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, methodology): “Zimmermann et al. (2007) used a number of code metrics on Eclipse.”
It's not a bug, it's a feature: How misclassification impacts bug prediction (2013, background): “This could also explain why predictors such as change bursts [8] or network metrics [10] work extremely well on industrial data sets, but poorly on open source data sets.”
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].”
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, background): “Since we preserve the original rate of defective modules in these samples (i.e., 21 percent), our samples are of 100, 300, 500, and 1,000 modules for an EPV value of 1, 3, 5, and 10, respectively (see Section 5.2 for details on sample size generation).”
Learning to rank relevant files for bug reports using domain knowledge (2014, background): “…behaviors and build interaction patterns; Nagappan et al. [33] utilize the frequency of similar changes described as change burts ; Hassan et al. [15] use code change complexity; Zimmermann et al. [46] build a dependency graph that implies an error-proneness for (cid:12)les linked to buggy models.”
An empirical study on software defect prediction with a simplified metric set (2014, methodology): “Although we used only static code metrics available in the literature [2, 8, 19, 36, 37], we are aware that other types of software metrics could exhibit different results.”
RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information (2015, unknown): “, the number of defects that affect various classes in a project during its life-time), which can, in turn, be used to build effective bug prediction solutions [24], [7], [43], [28], [41], [63], [21].”