Abstract
Despite the relevance of the software evolution phase, there are few characterization studies on recurrent evolution growth patterns and on their impact on software properties, such as coupling and cohesion. In this paper, we report a study designed to investigate whether the software evolution categories proposed by Lanza can be used to explain not only the growth of a system in terms of lines of code (LOC), but also in terms of metrics from the Chidamber and Kemerer (CK) object-oriented metrics suite. Our results show that high levels of recall (ranging on average from 52 to 72 %) are achieved when using LOC to predict the evolution of coupling and size. For cohesion, we have achieved smaller recall rates (<27 % on average).
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Notes
The representation of the evolution categories in this figure follows a linear model just for illustrative purposes. As can be checked, the definitions presented in the section do not assume a linear growth or contraction.
The original dataset includes a fifth system (Mylyn). However, we have not considered this system because the dataset includes information for only 47 biweeks of its evolution.
For Eclipse JDT and Eclipse PDE, we only considered the original time frame of the series available in the D’Ambros dataset.
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Acknowledgments
This research has been supported by the grants from FUNDEP/Santander, CAPES, FAPEMIG, and CNPq. We thank Marco D’Ambros for making the dataset with the historical values of the OO metrics publicly available and Andre Hora and Nicolas Anquetil for helping us with the Moose platform.
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Rocha, H., Couto, C., Maffort, C. et al. Mining the impact of evolution categories on object-oriented metrics. Software Qual J 21, 529–549 (2013). https://doi.org/10.1007/s11219-012-9186-7
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DOI: https://doi.org/10.1007/s11219-012-9186-7