Abstract
Amongst the critical actions needed to be undertaken before system testing, software fault prediction is imperative. Prediction models are used to identify fault-prone classes and contribute considerably to reduce the testing time, project risks, and resource and infrastructure costs. In the development of a prediction model, the interaction of metrics results in an improved predictive capability, accruing to the fact that metrics are often correlated and do not have a strict additive effect in a regression model.
Even though the interaction amongst metrics results in the model’s improved prediction capability, it also gives rise to a large number of predictors. This leads to Multiple Linear Regression (MLR) exhibiting a reduced level of performance, since a single predictive formula occupies the entire data space. The M5’ model tree has an edge over MLR in managing such interactions, by partitioning the data space into smaller regions.
The resulting hypothesis empirically establish that the M5’ model tree, when applied to these interactions, provides a greater degree of accuracy and robustness of the model as a whole when compared with MLR models.
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Corresponding author would like to thank Mr. Tanveer
Oberoi and Ms. Preeti Goyal to copyedit manuscript.
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Appendix A
Appendix A
CK Metric (Chidamber and Kemerer 1994) | Interpretation |
|---|---|
Weighted Methods per Class (WMC) | Identify complexity of class by finding the weighted sum of the complexity of the methods |
Coupling Between Object classes (CBO) | Identify the coupling between classes by considering the dependency of one class with other classes in the design |
Depth of the Inheritance Tree (DIT): | Identify the complexity of inheritance hierarchy by calculating the maximum length of a given class to the root class |
Lack of Cohesion metric (LCOM) | Identify cohesion with a class by counting the number of method pairs with zero similarity |
Number of Children (NOC): | Identify complexity of inheritance hierarchy by counting the number of immediate child classes that have inherited from a given class |
Response for the classes (RFC) | Identify the coupling between classes by calculating the sum of the number of local methods and the methods that can be called remotely |
OO (Object Oriented) | Interpretation |
NOM | Number of methods |
NOPM | Number of public methods |
NOPRM | Number of private methods |
NOMI | Number of methods inherited |
Fan-in | Number of other classes that reference the class |
Fan-out | Number of other classes referenced by the class |
NOAI | Number of attributes inherited |
NOA | Number of attributes |
NLOC | Number of lines of code |
NOPRA | Number of private attributes |
NOPA | Number of public attributes |
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Goyal, R., Chandra, P., Singh, Y. (2015). Comparison of M5’ Model Tree with MLR in the Development of Fault Prediction Models Involving Interaction Between Metrics. In: Elleithy, K., Sobh, T. (eds) New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering. Lecture Notes in Electrical Engineering, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-319-06764-3_19
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