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How changes affect software entropy: an empirical study by Gerardo Canfora, Luigi Cerulo, Marta Cimitile, Massimiliano Di Penta

pubblicato 21 dic 2013, 00:44 da Gerardo Canfora   [ aggiornato in data 24 feb 2014, 14:20 ]
Context Software systems continuously change for various reasons, such as adding new features, fixing bugs, or refactoring. Changes may either increase the source code complexity and disorganization, or help to reducing it. Aim This paper empirically investigates the relationship of source code complexity and disorganization—measured using source code change entropy—with four factors, namely the presence of refactoring activities, the number of developers working on a source code file, the participation of classes in design patterns, and the different kinds of changes occurring on the system, classified in terms of their topics extracted from commit notes. MethodWe carried out an exploratory study on an interval of the life-time span of four open source systems, namely ArgoUML, Eclipse-JDT, Mozilla, and Samba, with the aim of analyzing the relationship between the source code change entropy and four factors: refactoring activities, number of contributors for a file, participation of classes in design patterns, and change topics.Results The study shows that (i) the change entropy decreases after refactoring, (ii) files changed by a higher number of developers tend to exhibit a higher change entropy than others, (iii) classes participating in certain design patterns exhibit a higher change entropy than others, and (iv) changes related to different topics exhibit different change entropy, for example bug fixings exhibit a limited change entropy while changes introducing new features exhibit a high change entropy.Conclusions Results provided in this paper indicate that the nature of changes (in particular changes related to refactorings), the software design, and the number of active developers are factors related to change entropy. Our findings contribute to understand the software aging phenomenon and are preliminary to identifying better ways to contrast it.
Empirical Software Engineering, February 2014, Volume 19, Issue 1, pp. 1-38: http://link.springer.com/article/10.1007%2Fs10664-012-9214-z
DOI: 10.1007/s10664-012-9214-z
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