Open Access
Using Object-Oriented Design Metrics to Predict Software Defects 1*
Marian Jureczko,Diomidis D. Spinellis +1 more
- 01 Jan 2010
TL;DR: This study was made possible through the creation of a new metric calculation tool, Ckjm, that calculates metrics that have been recommended as good quality indicators and that have empirically proven their usability in quality or defect prediction.
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Abstract: Many object-oriented design metrics have been developed [1,3,8,17,24] to help in predict software defects or evaluate design quality. Since a defect prediction model may give crucial clues about the distribution and location of defects and, thereby, test prioritization, accurate prediction can save costs in the testing process. Considerable research has been performed on defect prediction methods; see the surveys by Purao and Vaishnavi [22] and by Wahyudin et al. [25], unfortunately few results appear at statistically significant level. Therefore, further empirical validation is necessary to prove the usefulness of the metrics and software prediction models in industrial practice. Our study was made possible through the creation of a new metric calculation tool. There are many tools that calculate object-oriented metrics. What is the reason to create another one? In fact the situation is not so perfect. The available programs are either extremely inefficient (sometimes they do not work with big software projects at all), not available as open source and therefore difficult to reason about their results, or incomplete — the set of calculated metrics is not wide enough. It is extremely hard to find a tool that calculates all metrics from the Chidamber and Kemerer (C&K) metrics suite [3]. Having both, C&K and QMOOD metrics suites [1] in one tool is even rarer, and according to the authors' knowledge there is no other tool, that calculates metrics suggested by Tang et al. [24]. Ckjm calculates metrics that have been recommended as good quality indicators. There are several works that investigate the C&K metric suite and that have empirically proven their usability in quality or defect prediction [2, 10, 11, 20]. There are recommendations about QMOOD metrics suite [1, 20] and the quality oriented extension of C&K [24] too. Ckjm does not offer a GUI and its focus is not on
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Citations
Towards identifying software project clusters with regard to defect prediction
Marian Jureczko,Lech Madeyski +1 more
- 12 Sep 2010
TL;DR: The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used.
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction
TL;DR: CPDP is still a challenge and requires more research before trustworthy applications can take place and this work synthesises literature to understand the state-of-the-art in CPDP with respect to metrics, models, data approaches, datasets and associated performances.
An empirical study on software defect prediction with a simplified metric set
TL;DR: The experimental results indicate that the choice of training data for defect prediction should depend on the specific requirement of accuracy and the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.
MAHAKIL: diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction
Kwabena Ebo Bennin,Jacky Keung,Passakorn Phannachitta,Akito Monden,Solomon Mensah +4 more
- 27 May 2018
TL;DR: In this article, a novel and efficient synthetic oversampling approach for software defect datasets that is based on the chromosomal theory of inheritance is introduced. But it is not suitable for training a model on highly imbalanced data.
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References
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A metrics suite for object oriented design
Shyam R. Chidamber,Chris F. Kemerer +1 more
- 02 Sep 2011
TL;DR: This research addresses the needs for software measures in object-orientation design through the development and implementation of a new suite of metrics for OO design, and suggests ways in which managers may use these metrics for process improvement.
A Complexity Measure
TL;DR: Several properties of the graph-theoretic complexity are proved which show, for example, that complexity is independent of physical size and complexity depends only on the decision structure of a program.
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Thomas J. McCabe
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TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.
5.1K
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
TL;DR: A framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository, showing an appealing degree of predictive accuracy, which supports the view that metric-based classification is useful.
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Metrics and Models in Software Quality Engineering
Stephen H. Kan,Brian Thomas +1 more
- 01 Dec 1994
TL;DR: Stephen H. Kan is responsible for IBM Rochester's software quality strategy and plans, quality assessment, software measurements, and statistical analysis.
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