The prediction of faulty classes using object-oriented design metrics

By K. El Emam, Walcélio L. Melo, Javam C. Machado. Journal of Systems and Software, 2001

433 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/0e3d7e25dd2aefd58eacf3f85305c665cb2e567b

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Citations in the corpus

Software fault prediction metrics: A systematic literature review (2013, background): “According to various authors [62,66,67, 81,149,89,153,128,132,136,53,137] the best metrics from the CK metrics suite are CBO, WMC and RFC, which were effective across all groups (Table 5).”
An extensive comparison of bug prediction approaches (2010, methodology): “used the CK metrics in conjunction with Briand’s coupling metrics [3] to predict faults on a commercial Java system [4].”
Application of neural networks for software quality prediction using object-oriented metrics (2003, background): “Emanm and Melo [4] have constructed a model to predict which classes in a future release of a commercial Java application will be faulty.”
A large-scale empirical study of just-in-time quality assurance (2013, methodology): “Prior work used product metrics such as McCabe’s cyclomatic complexity metric [33] and the Chidamber and Kemerer (CK) metrics suite [9] and code size (measured in lines of code) [1], [12], [15], [21], [29].”
Software fault prediction: A literature review and current trends (2011, background): “Emam, Melo, and Machado (2001) predicted the fault-prone classes by using logistic regression and class level metrics on a commercial Java application.”
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (2008, background): “Furthermore, its choice is motivated by the fact that the split-sample setup is the prevailing approach to assess predictive accuracy in software defect prediction [15], [16], [23], [28], [32], [33], [34], [37].”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, methodology): “We performed three sets of experiments aimed at (1) comparing the approaches across different systems, (2) testing whether the differences in performance are statistically significant, and (3) investigating the stability of approaches across different learners.”
Software defect prediction using Bayesian networks (2012, methodology): “ROC analysis is used in the literature and considers TP rate (also called sensitivity) and FP rate (also called false alarm rate) together [11].”
Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects (2003, methodology): “Some researchers in the past have used size-like measures, such as cyclomatic complexity of methods in their weighting scheme [24], [35].”
Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, methodology): “One use of cohesion metrics in software engineering is to predict faults in classes [ 30 ], [39], [69].”