Empirical validation of object-oriented metrics on open source software for fault prediction

By Tibor Gyimóthy, Rudolf Ferenc, István Siket. IEEE Transactions on Software Engineering, 2005

960 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/1051280d2b825c04f27d231aba0f8284bb297880

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Citations in the corpus

Software fault prediction metrics: A systematic literature review (2013, methodology): “1 Model performance is inﬂuenced by a modeling technique [9,16,20,29,30] and metrics [89,138,130,88,66].”
Classifying Software Changes: Clean or Buggy? (2008, methodology): “This is on par with the best recent work on bug prediction in the research literature [12], [39], with the added benefit that the granularity of prediction is smaller: It is localized to the section of text related to a change instead of a whole file or function/method.”
A large-scale empirical study of just-in-time quality assurance (2013, methodology): “In order to draw more general conclusions, Gyimothy et al. [17] also analyzed the relationship of Chidamber and Kemerer’s metrics with software quality using data collected from the open source Mozilla project across several releases (version 1.0-1.6).”
Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values (2008, background): “Schwartz’s theory has been used to explain systematic relationship patterns between value priorities and a variety of attitudes, beliefs and behaviors, such as consumer purchases (Puohiniemi, 1995), environmental behavior (e.g., Bianchi & Rosova, 1992; Grunert & Juhl, 1995 ), voting behavior (Barne”
Cross-project defect prediction: a large scale experiment on data vs. domain vs. process (2009, background): “Gyimothy et al. [ 14 ] mined the CK metrics data from Mozilla versions (1.0- 1.6) and investigated their relationship and ability to predict fault classes.”
A Systematic Literature Review on Fault Prediction Performance in Software Engineering (2012, neutral): “These papers are: [[8]], [[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]],…”
An empirical study on software defect prediction with a simplified metric set (2014, methodology): “ntative 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]. In addition, Figure 8 presents the prediction results achieved by predictors built wit”
Using the Conceptual Cohesion of Classes for Fault Prediction in Object-Oriented Systems (2008, methodology): “All analyses in this case study are applied with the same settings as those described by Gyimo´thy et al. [ 39 ].”
A systematic review of machine learning techniques for software fault prediction (2015, background): “Hence, total number of 122 studies [1, 2, 20-139] were identified for further processing and analysis.”
Increasing diversity: Natural language measures for software fault prediction (2009, methodology): “A number of studies have found correlations between structural characteristics of software modules and problems, such as change or defect proneness (Bell et al., 2006; Fenton and Ohlsson, 2000; Gyimóthy et al., 2005; Kokol et al., 2001; Koru and Tian, 2007; Menzies et al., 2007; Munson and Khoshgoft”
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.”
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults (2006, result): “Our results, based on a public domain NASA data set, indicate that 1) most of these design metrics are statistically related to fault-proneness of classes across fault severity, and 2) the prediction capabilities of the investigated metrics greatly depend on the severity of faults.”
Is it a bug or an enhancement?: a text-based approach to classify change requests (2008, methodology): “[12], multivariate logistic regression represents an effective technique to study the relationship between the fault proneness of classes and source code metrics.”
Software fault prediction: A literature review and current trends (2011, background): “Gyimothy, Ferenc, and Siket (2005) studied to validate object oriented metrics for fault prediction by using logistic regression, linear regression, decision trees, and neural networks on Mozilla open source project.”
An extensive comparison of bug prediction approaches (2010, background): “The jury is still out on the relative performance of these approaches.”
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, methodology): “In the past, Basili et al. [6], Briand et al. [13], and, more recently, Ferenc et al. [ 15 ], conducted correlations between the CK metrics and showed varying degrees of correlation between the CK class metrics.”
Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, methodology): “This result explains why some studies have encountered contradicting conclusions, when run on other datasets, or with other learning algorithms, such as the opposing conclusions of Gyimothy et al. (2005) , and of Fenton and Ohlsson (2000).”