Abstract—In systems developed without aspect-oriented programming, code implementing a crosscutting concern may be spread over many different parts of a system. Identifying such code automatically could be of great help during maintenance of the system. First of all, it allows a developer to more easily find the places in the code that must be changed when the concern changes and, thus, makes such changes less time consuming and less prone to errors. Second, it allows the code to be refactored to an aspect-oriented solution, thereby improving its modularity. In this paper, we evaluate the suitability of clone detection as a technique for the identification of crosscutting concerns. To that end, we manually identify five specific crosscutting concerns in an industrial C system and analyze to what extent clone detection is capable of finding them. We consider our results as a stepping stone toward an automated “aspect miner ” based on clone detection. Index Terms—Clone detection, reverse engineering, aspect-oriented programming, crosscutting concerns, aspect mining. 1

The fact that crosscutting concerns (aspects) cannot be well modularized in object oriented software is an impediment to program comprehension: the implementation of a concern is typically scattered over many locations and tangled with the implementation of other concerns, resulting in a system that is hard to explore and understand. Aspect mining aims to identify crosscutting concerns in a system, thereby improving the system’s comprehensibility and enabling migration of existing (object-oriented) programs to aspect-oriented ones. In this paper, we compare three aspect mining techniques that were developed independently by different research teams: fan-in analysis, identifier analysis and dynamic analysis. We apply each technique to the same case (JHotDraw) and mutually compare the individual results of each technique based on the discovered aspects and on the level of detail and quality of those aspects. Strengths, weaknesses and underlying assumptions of each technique are discussed, as well as their complementarity. We conclude with a discussion of possible ways to combine the techniques in order to achieve a better overall aspect-mining technique.

Aspect mining identifies cross-cutting concerns in a program to help migrating it to an aspect-oriented design. Such concerns may not exist from the beginning, but emerge over time. By analysing where developers add code to a program, our history-based aspect mining (HAM) identifies and ranks cross-cutting concerns. We evaluated the effectiveness of our approach with the history of three open-source projects. HAM scales up to industrial-sized projects: for example, we were able to identify a locking concern that cross-cuts 1 284 methods in Eclipse. Additionally, the precision of HAM increases with project size and history; for Eclipse, it reaches 90 % for the top-10 candidates. 1.

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This paper presents a human guided automated approach to refactoring object oriented programs to the aspect oriented paradigm. The approach is based upon the iterative application of four steps: discovery, enabling, selection, and refactoring. After discovering potentially applicable refactorings, the enabling step transforms the code to improve refactorability. During the selection phase the particular refactorings to apply are chosen. Finally, the refactoring phase transforms the code by moving the selected code to a new aspect. This paper presents the results of an evaluation in which one of the crosscutting concerns of a 40,000 LoC program (JHotDraw) is refactored. 1.

Crosscutting concerns are software system features whose implementation is spread across many modules as tangled and scattered code. Identifying such code helps developers to change the concern and/or re-factor it to an aspect. This paper evaluates the suitability of line co-change as a technique for the identification of crosscutting concerns. A line co-change aim at identifying source code lines that have been changed together in a commit transaction performed using a versioning system such as CVS. Promising results have been obtained by evaluating the approach to identify four crosscutting concerns present in an opensource system, JHotDraw. The paper also shows that line co-change can be effectively complemented with clone detection to improve the performance achieved by the separate approaches. 1.

Aspect Oriented Programming (AOP) provides mechanisms for the separation of crosscutting concerns – functionalities scattered through the system and tangled with the base code. Existing systems are a natural testbed for the AOP approach, since they often contain several crosscutting concerns which could not be modularized using traditional programming constructs. This paper presents an automated approach to the problem of migrating systems developed according to the Object Oriented Programming (OOP) paradigm into Aspect Oriented Programming (AOP). A simple set of six refactorings has been defined to transform OOP to AOP and has been implemented in the AOP-Migrator tool, an Eclipse plug-in. A set of enabling transformations from OOP to OOP complement the initial set of refactorings. The paper presents the results of four case studies, which use the approach to migrate selected crosscutting concerns from medium–sized Java programs (in the range 10K to 40K lines of code) into equivalent programs in AspectJ. The case study results show the feasibility of the migration and indicate the importance of the enabling transformations as a pre-processing step. 1

Aspect mining is a reverse engineering process that aims at finding crosscutting concerns in existing systems. This paper proposes an aspect mining approach based on determining methods that are called from many different places, and hence have a high fan-in, which can be seen as a symptom of crosscutting functionality. The approach is semi-automatic, and consists of three steps: metric calculation, method filtering, and call site analysis. Carrying out these steps is an interactive process supported by an Eclipse plug-in called FINT. Fan-in analysis has been applied to three open source Java systems, totaling around 200,000 lines of code. The most interesting concerns identified are discussed in detail, which includes several concerns not previously discussed in the aspect-oriented literature. The results show that a significant number of crosscutting concerns can be recognized using fan-in analysis, and each of the three steps can be supported by tools.

This paper offers a first, in-breadth survey and comparison of current aspect mining tools and techniques. It focuses mainly on automated techniques that mine a program’s static or dynamic structure for candidate aspects. We present an initial comparative framework for distinguishing aspect mining techniques, and assess known techniques against this framework. The results of this assessment may serve as a roadmap to potential users of aspect mining techniques, to help them in selecting an appropriate technique. It also helps aspect mining researchers to identify remaining open research questions, possible avenues for future research, and interesting combinations of existing techniques.

Abstract. Understanding a software system at source-code level requires understanding the different concerns that it addresses, which in turn requires a way to identify these concerns in the source code. Whereas some concerns are explicitly represented by program entities (like classes, methods and variables) and thus are easy to identify, crosscutting concerns are not captured by a single program entity but are scattered over many program entities and are tangled with the other concerns. Because of their crosscutting nature, such crosscutting concerns are difficult to identify, and reduce the understandability of the system as a whole. In this paper, we report on a combined experiment in which we try to identify crosscutting concerns in the JHotDraw framework automatically. We first apply three independently developed aspect mining techniques to JHotDraw and evaluate and compare their results. Based on this analysis, we present three interesting combinations of these three techniques, and show how these combinations provide a more complete coverage of the detected concerns as compared to the original techniques individually. Our results are a first step towards improving the understandability of a system that contains crosscutting concerns, and can be used as a basis for refactoring the identified crosscutting concerns into aspects. 1