Mining metrics to predict component failures

By N. Nagappan, Thomas Ball, Andreas Zeller. International Conference on Software Engineering, 2006

861 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/27cfd39ddbdcfc09124f94a995ef8bdbd3e4291b

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Citations in the corpus

Software fault prediction metrics: A systematic literature review (2013, methodology): “McCabe’s cyclomatic complexity was a good predictor of software fault proneness in [127,156,112,111,121, 92,75], but not in [64,87,141,148].”
An investigation on the feasibility of cross-project defect prediction (2011, background): “Nagappan et al. (2006) studied the generalization ability of prediction models for post-release defects.”
Towards identifying software project clusters with regard to defect prediction (2010, background): “[16] extended the state of the art through analyzing whether predictors obtained from one project history are applicable to other projects.”
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process (2009, neutral): “Cross-project Defect Prediction A Large Scale Experiment on Data vs. Domain vs. Process Thomas Zimmermann Nachiappan Nagappan Harald Gall Microsoft Research Microsoft Research University of Zurich tzimmer@microsoft.com nachin@microsoft.com gall@ifi.uzh.ch Emanuel Giger University of Zurich…”
Transfer defect learning (2013, methodology): “In addition, we propose a novel transfer defect learning approach, TCA+, by extending TCA.”
Online Defect Prediction for Imbalanced Data (2015, background): “…Tan Sashank Dara Caleb Mayeux University of Waterloo, Canada University of Waterloo, Canada Cisco Systems, India Cisco Systems, USA m9tan@uwaterloo.ca lintan@uwaterloo.ca sadara@cisco.com cmayeux@cisco.com Abstract Many defect prediction techniques are proposed to improve software reliability.”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, background): “, lines of code, complexity) (Basili et al. 1996; Ohlsson and Alberg 1996; Briand et al. 1999; El Emam et al. 2001; Subramanyam and Krishnan 2003; Gyimóthy et al. 2005; Nagappan and Ball 2005a; Nagappan et al. 2006), process metrics (e.”
Deep Learning for Just-in-Time Defect Prediction (2015, neutral): “Name Description NS The number of modified subsystems [19] ND The number of modified directories [19] NF The number of modified files [20] Entropy Distribution of modified code across each file [21] LA Lines of code added [22] LD Lines of code deleted [22] LT Lines of code in a file before the chang”
Predicting defects using network analysis on dependency graphs (2008, neutral): “D.2.9 [Software Engineering]:”
Predicting faults using the complexity of code changes (2009, background): “Fault predictors are usually project speciﬁc and vary in performance from one project to the next (similar observations on commercial systems were noted by Nagappan et al. [27]).”
Learning to rank relevant files for bug reports using domain knowledge (2014, methodology): “Nagappan et al. [32] apply principle component analysis (PCA), while Bell et al. [3] and Ostrand et al. [35] use negative binomial regression to build models that predict fault-prone (cid:12)les.”
Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities (2011, neutral): “[39] found that sets of complexity metrics are correlated with postrelease defects using five major Microsoft product components, including Internet Explorer 6.”
A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction (2008, background): “[17] arrived at similar conclusions as they stated: “However, there is no single set of complexity metrics that could act as a universally best defect predictor”.”
Dealing with noise in defect prediction (2011, methodology): “Defect prediction is a very active area in software engineering research [7, 10, 11, 13, 18, 19, 33, 35].”
A Systematic Literature Review on Fault Prediction Performance in Software Engineering (2012, neutral): “…[[9]], [[10]], [[11]], [[18]], [[21]], [[29]], [[31]], [[32]], [[37]], [[51]], [[56]], [[69]], [[73]], [[74]], [[76]], [[83]], [[86]], [[92]], [[98]], [[109]], [[110]], [[116]], [[117]], [[118]], [[120]], [[122]], [[127]], [[133]], [[135]], [[154]], [[160]], [[163]], [[164]], [[190]], [[203]].”
An extensive comparison of bug prediction approaches (2010, background): “Based on the results, we discuss the performance and stability of the approaches with respect to our benchmark and deduce a number of insights on bug prediction models.”
Heterogeneous Defect Prediction (2015, background): “Defect prediction models have been reported at large industrial companies such as Google [42], Microsoft [59], AT&T [64], and Samsung [35].”