Exploring the Relation Between Co-changes and Architectural Smells

The interplay between Maintainability and Reliability can be particularly complex and different kinds of trade-offs may arise when developers try to optimise for either one of these two qualities. To further understand how Maintainability and Reliability influence each other, we perform an empirical study using architectural smells and source code file co-changes as proxies for these two qualities, respectively. The study is designed using an exploratory multiple-case case study following well-know guidelines and using fourteen open source Java projects. Three different research questions are identified and investigated through statistical analysis. Co-changes are detected by using both a state-of-the-art algorithm and a novel approach. The three architectural smells selected are among the most important from the literature and are detected using open source tools. The results show that 50% of co-changes eventually end up taking part in an architectural smell. Moreover, statistical tests indicate that in 50% of the projects, files and packages taking part in smells are more likely to co-change than non-smelly files. Finally, co-changes were also found to appear before smells 90% of the times a smell and a co-change appear in the same file pair. Our findings show that Reliability is indirectly affected by low levels of Maintainability even at the architectural level. This is because low-quality components require more frequent changes by the developers, increasing chances to eventually introduce faults

On the relation between architectural smells and source code changes

Although architectural smells are one of the most studied type of architectural technical debt, their impact on maintenance effort has not been thoroughly investigated. Studying this impact would help to understand how much technical debt interest is being paid due to the existence of architecture smells and how this interest can be calculated. This work is a first attempt to address this issue by investigating the relation between architecture smells and source code changes. Specifically, we study whether the frequency and size of changes are correlated with the presence of a selected set of architectural smells. We detect architectural smells using the Arcan tool, which detects architectural smells by building a dependency graph of the system analyzed and then looking for the typical structures of the architectural smells. The findings, based on a case study of 31 open-source Java systems, show that 87% of the analyzed commits present more changes in artifacts with at least one smell, and the likelihood of changing increases with the number of smells. Moreover, there is also evidence to confirm that change frequency increases after the introduction of a smell and that the size of changes is also larger in smelly artifacts. These findings hold true especially in Medium–Large and Large artifacts

A Method for Assessing Class Change Proneness

© 2017 Association for Computing Machinery ACM. Change proneness is a quality characteristic of software artifacts that represents their probability to change in the future due to: (a) evolving requirements, (b) bug fixing, or (c) ripple effects. In the literature, change proneness has been associated with many negative consequences along software evolution. For example, artifacts that are change-prone tend to produce more defects, and accumulate more technical debt. Therefore, identifying and monitoring modules of the system that are change-prone is of paramount importance. Assessing change proneness requires information from two sources: (a) the history of changes in the artifact as a proxy of how frequently the artifact itself is changing, and (b) the source code structure that affects the probability of a change being propagated among artifacts. In this paper, we propose a method for assessing the change proneness of classes based on the two aforementioned information sources. To validate the proposed approach, we performed a case study on five open-source projects. Specifically, we compared the accuracy of the proposed approach to the use of other software metrics and change history to assess change proneness, based on the 1061-1998 IEEE Standard on Software Measurement. The results of the case study suggest that the proposed method is the most accurate and reliable assessor of change proneness. The high accuracy of the method suggests that the method and accompanying tool can effectively aid practitioners during software maintenance and evolution