Abstract
Software module clustering is the problem of automatically partitioning the structure of a software system using low-level dependencies in the source code to understand and improve the system’s architecture. Munch, a clustering tool based on search-based software engineering techniques, was used to modularise a unique dataset of sequential source code software versions. This paper investigates whether the dataset used for the modularisation resembles a random graph by computing the probabilities of observing certain connectivity. Modularisation will not be possible with data that resembles random graphs. Thus, this paper demonstrates that our real world time-series dataset does not resemble a random graph except for small sections where there were large maintenance activities. Furthermore, the random graph metric can be used as a tool to indicate areas of interest in the dataset, without the need to run the modularisation.
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Arzoky, M., Swift, S., Counsell, S., Cain, J. (2014). A Measure of the Modularisation of Sequential Software Versions Using Random Graph Theory. In: Dingsøyr, T., Moe, N.B., Tonelli, R., Counsell, S., Gencel, C., Petersen, K. (eds) Agile Methods. Large-Scale Development, Refactoring, Testing, and Estimation. XP 2014. Lecture Notes in Business Information Processing, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-319-14358-3_10
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DOI: https://doi.org/10.1007/978-3-319-14358-3_10
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