SoQueT: Query-Based Documentation of Crosscutting Concerns
ICSE '07 Proceedings of the 29th international conference on Software Engineering
PASTE '07 Proceedings of the 7th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Bridging the gap between aspect mining and refactoring
Proceedings of the 3rd workshop on Linking aspect technology and evolution
On some criteria for comparing aspect mining techniques
Proceedings of the 3rd workshop on Linking aspect technology and evolution
Identifying Crosscutting Concerns Using Fan-In Analysis
ACM Transactions on Software Engineering and Methodology (TOSEM)
Inferring structural patterns for concern traceability in evolving software
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
An integrated crosscutting concern migration strategy and its semi-automated application to JHotDraw
Automated Software Engineering
Identifying cross-cutting concerns using software repository mining
Proceedings of the Joint ERCIM Workshop on Software Evolution (EVOL) and International Workshop on Principles of Software Evolution (IWPSE)
A systematic review on mining techniques for crosscutting concerns
Proceedings of the 28th Annual ACM Symposium on Applied Computing
| Hi-index | 0.00 |
The increasing number of aspect mining techniques proposed in literature calls for a methodological way of comparing and combining them in order to assess, and improve on, their quality. This paper addresses this situation by proposing a common framework based on crosscutting concern sorts which allows for consistent assessment, comparison and combination of aspect mining techniques. The framework identifies a set of requirements that ensure homogeneity in formulating the mining goals, presenting the results and assessing their quality. We demonstrate feasibility of the approach by retrofitting an existing aspect mining technique to the framework, and by using it to design and implement two new mining techniques. We apply the three techniques to a known aspect mining benchmark and show how they can be consistently assessed and combined to increase the quality of the results. The techniques and combinations are implemented in FINT, our publicly available free aspect mining tool.