Abstract
Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.
Highlights
Software Change Impact Analysis (CIA) is an essential technique for identifying the potential ripple effects caused by software changes during software maintenance and evolution (Briand et al 1999; Wilkie and Kitchenham 2000)
The work we present is based on the three following goals: G1: to establish with a larger sample of OSS projects whether the semantic coupling between classes using the class names of Java files produces comparable results to using the corpora of the classes content (Ajienka and Capiluppi 2016); Fig. 2 Motivating example: structural coupling → co-evolution (Yu 2007) and semantic → co-evolution
A Chi-squared test of independence was carried out to investigate the independence of the semantic coupling metrics measured using: 1. A corpora based technique (VSM) and 2
Summary
Software Change Impact Analysis (CIA) is an essential technique for identifying the potential ripple effects caused by software changes during software maintenance and evolution (Briand et al 1999; Wilkie and Kitchenham 2000). Addressed at establishing a link between coupling and co-change, have found that the set of co-changed classes was much larger compared to the set of structurally coupled classes (Oliva and Gerosa 2011, 2015; Fluri et al 2005, Geipel and Schweitzer 2012). This implies that not all of the change dependencies are related to structural dependencies and there could be other reasons for software artefacts to be change dependent (Oliva and Gerosa 2011). Software that is not flexible or tolerant to modification is usually destined to abandonment or replacement (Oliva and Gerosa 2012)
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