Empirical Analysis of CK Metrics for Object-Oriented Design Complexity: Implications for Software Defects

By Ramanath Subramanyam, Mayuram S. Krishnan. IEEE Trans. Software Eng., 2003

726 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/3acc32ea0ded0a9e2ef5759c2f4970bf5ea18508

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Citations in the corpus

Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, methodology): “For more details on regression analyses and their applications on using metrics to detect fault prone classes, the reader is referred to our previous work [6], [13], [39], [72].”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, background): “ Subramanyam and Krishnan (2003 ) u sed the CK metrics on ac ommercial C++/Java system.”
Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes (2007, background): “OO metrics validation studies using fault-proneness as a quality indicator that have appeared since the publication of [7] include [11], [12], [13], [14], [15].”
Static analysis tools as early indicators of pre-release defect density (2005, background): “These metrics can be a useful early internal indicators of externally-visible product quality [1, 25, 26].”
An empirical study on software defect prediction with a simplified metric set (2014, background): “, a positive correlation exists between LOC and defects [9]); (3) as the representative complexity metrics, both CBO and CE are important indicators for fault-prone classes in terms of software coupling and dependency, which have been commonly used to defect-proneness prediction [60, 61, 62].”
Software fault prediction metrics: A systematic literature review (2013, methodology): “[40,39] DAC Data abstraction coupling [71,67,53,69,65,66,90,138]”
A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction (2008, positive): “Subramanyam and Krishnan [26] confirmed these results.”
Mining metrics to predict component failures (2006, background): “The study by Subramanyam and Krishnan [21] presents a survey on eight more empirical studies, all showing that OO metrics are significantly associated with defects.”
Empirical validation of object-oriented metrics on open source software for fault prediction (2005, methodology): “Just like WMC, this metric was found significant in all three studies [1], [21], [22]—which also confirms that the CBO is the best among these metrics.”
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults (2006, methodology): “Such models can be used to guide the decision-making of software development managers seeking to produce high-quality software on time and within budget [25], and indeed the last three decades has seen proposed many software fault prediction models [1], [ 2 ], [4], [7], [8], [9], [10], [11], [12], [”
Predicting defects using network analysis on dependency graphs (2008, neutral): “D.2.9 [Software Engineering]:”
Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models (2015, background): “, number of changes, recent activity), product metrics [20, 41, 43, 44, 60] (e.”
An extensive comparison of bug prediction approaches (2010, methodology): “used CK metrics on a commercial C++/Java system [5]; Gyimothy et al.”