Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. Deciding which pointcuts have broken because of changes made to the base-code is a daunting task, especially in large and complex systems. This project represents an automated approach that recommends a set of pointcuts that are likely to require modification due to a particular change in the base-code. Our hypothesis is that join points captured by a pointcut exhibit a varying degree of common structural characteristics on multiple levels. We use patterns describing such commonality to recommend pointcuts that have potentially broken to the developer, as well as point the developer in a direction in which the pointcut should be altered. We implemented our approach as an extension to the popular Mylyn Eclipse IDE plugin, which maintains focused contexts of entities relevant to the current task. We show that this approach is useful in revealing broken pointcuts by applying it to multiple versions of several open source projects and evaluating the accuracy of the recommendations produced against actual modifications made on these systems.
Pointcut fragility is a well-documented problem in Aspect-Oriented Programming; changes to the base-code can lead to join points incorrectly falling in or out of the scope of pointcuts. In this project, we use an automated approach which limits fragility problems by providing mechanical assistance in pointcut maintenance. The approach is based on harnessing arbitrarily deep structural commonalities between program elements corresponding to join points selected by a pointcut. The extracted patterns are then applied to later versions to offer suggestions of new join points that may require inclusion. To illustrate that the motivation behind our proposal is well-founded, we first empirically establish that join points captured by a single pointcut typically portray a significant amount of unique structural commonality by analyzing patterns extracted from 23 AspectJ programs. Then, we demonstrate the usefulness of our technique by rejuvenating pointcuts in multiple versions of 3 of these programs. The results show that our parameterized heuristic algorithm was able to automatically infer new join points in subsequent versions with an average recall of 0.93. Moreover, these join points appeared, on average, in the top 4th percentile of the suggestions, indicating that the results were precise.