Although one of the main promises of aspect-oriented (AO) programming techniques is to promote better software changeability than objectoriented (OO) techniques, there is no empirical evidence on their efficacy to prolong design stability in realistic development scenarios. For instance, no investigation has been performed on the effectiveness of AO decompositions to sustain overall system modularity and minimize manifestation of ripple-effects in the presence of heterogeneous changes. This paper reports a quantitative case study that evolves a real-life application to assess various facets of design stability of OO and AO implementations. Our evaluation focused upon a number of system changes that are typically performed during software maintenance tasks. They ranged from successive re-factorings to more broadly-scoped software increments relative to both crosscutting and non-crosscutting concerns. The study included an analysis of the application in terms of modularity, change propagation, concern interaction, identification of ripple-effects and adherence to well-known design principles.

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Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Detection of code smells is challenging for developers and their informal definition leads to the implementation of multiple detection techniques and tools. This paper evaluates and compares four code smell detection tools, namely inFusion, JDeodorant, PMD, and JSpIRIT. These tools were applied to different versions of the same software systems, namely MobileMedia and Health Watcher, to calculate the accuracy and agreement of code smell detection tools. We calculated the accuracy of each tool in the detection of three code smells: God Class, God Method, and Feature Envy. Agreement was calculated among tools and between pairs of tools. One of our main findings is that the evaluated tools present different levels of accuracy in different contexts. For MobileMedia, for instance, the average recall varies from 0 to 58% and the average precision from 0 to 100%, while for Health Watcher the variations are 0 to 100% and 0 to 85%, respectively. Regarding the agreement, we found that the overall agreement between tools varies from 83 to 98% among all tools and from 67 to 100% between pairs of tools. We also conducted a secondary study of the evolution of code smells in both target systems and found that, in general, code smells are present from the moment of creation of a class or method in 74.4% of the cases of MobileMedia and 87.5% of Health Watcher.

Code smells are anomalies often caused by the way concerns are realized in the source code. Their identification might depend on properties governing the structure of individual concerns and their inter-dependencies in the system implementation. Although code visualization tools are increasingly applied to support anomaly detection, they are mostly limited to represent modular structures, such as methods, classes and packages. This paper presents a multiple views approach that enriches four categories of code views with concern properties, namely: (i) concern's package-classmethod structure, (ii) concern's inheritance-wise structure, (iii) concern dependency, and (iv) concern dependency weight. An exploratory study was conducted to assess the extent to which visual views support code smell detection. Developers identified a set of well-known code smells on five versions of an opensource system. Two important results came out of this study. First, the concern-driven views provided useful support to identify God Class and Divergent Change smells. Second, strategies for smell detection supported by the multiple concern views were uncovered.

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