Empirical study of fault prediction for open‐source systems using the Chidamber and Kemerer metrics

IET Software ◽  
2014 ◽  
Vol 8 (3) ◽  
pp. 113-119 ◽  
Author(s):  
Raed Shatnawi
Author(s):  
E. NASSERI ◽  
S. COUNSELL

In this paper, we present an empirical study to investigate whether class movement and re-location within inheritance hierarchy can be predicted based on size, coupling and cohesion for four Java open-source systems. Our results showed that class movement may not be predicted based on coupling and cohesion, and while class size was found to be a factor that may help predict class movement, it does not per se predict class movement within an inheritance hierarchy. We found a significantly higher odds ratio for larger classes to be moved within an inheritance hierarchy than that of smaller classes, suggesting that, counter-intuitively, larger classes tend to be more susceptible to movement than smaller classes. We also found that in the four systems, while classes with high coupling, low cohesion and larger size tended to be moved within their respective inheritance hierarchy, classes with high coupling, low cohesion and relatively smaller size tended to be candidate classes for deletion. Finally, while we found that class coupling and size tended to rise as the systems evolved we found no statistical support for class cohesion to decline. Directed towards developers and project managers, the message that the research conveys is that excessive growth in class size is at the root of a class' deterioration in terms of movement; developmental controls should be exercised to avoid such growth.


2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Raed Shatnawi ◽  
Qutaibah Althebyan

Context. Software metrics are surrogates of software quality. Software metrics can be used to find possible problems or chances for improvements in software quality. However, software metrics are numbers that are not easy to interpret. Previous analysis of software metrics has shown fat tails in the distribution. The skewness and fat tails of such data are properties of many statistical distributions and more importantly the phenomena of the power law. These statistical properties affect the interpretation of software quality metrics. Objectives. The objective of this research is to validate the effect of power laws on the interpretation of software metrics. Method. To investigate the effect of power law properties on software quality, we study five open-source systems to investigate the distribution and their effect on fault prediction models. Results. Study shows that power law behavior has an effect on the interpretation and usage of software metrics and in particular the CK metrics. Many metrics have shown a power law behavior. Threshold values are derived from the properties of the power law distribution when applied to open-source systems. Conclusion. The properties of a power law distribution can be effective in improving the fault-proneness models by setting reasonable threshold values.


Author(s):  
Muhammad Waseem ◽  
Peng Liang ◽  
Mojtaba Shahin ◽  
Aakash Ahmad ◽  
Ali Rezaei Nassab
Keyword(s):  

2021 ◽  
Author(s):  
Mívian Ferreira ◽  
Diego Golçalves ◽  
Kecia Ferreira ◽  
Mariza Bigonha

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