Automated Inference of Pointcuts in Aspect-Oriented Refactoring

Abstract

Software refactoring is the process of reorganizing the internal structure of code while preserving the external behavior. Aspect-Oriented Programming (AOP) provides new modularization of software systems by encapsulating crosscutting concerns. Based on these two techniques, aspect-oriented (AO) refactoring restructures crosscutting elements in code. AO refactoring includes two steps: aspect mining (identification of aspect candidates in code) and aspect refactoring (semantic-preserving transformation to migrate the aspect-candidate code to AO code). Aspect refactoring clusters similar join points together for the aspect candidates and encapsulates each cluster with an effective pointcut definition. With the increase in size of the code and crosscutting concerns, it is tedious to manually identify aspects and their corresponding join points, cluster the join points, and infer pointcut expressions. Therefore, there is a need to automate the process of AO refactoring. This paper proposes an automated approach that identifies aspect candidates in code and infers pointcut expressions for these aspects. Our approach mines for aspect candidates, identifies the join points for the aspect candidates, clusters the join points, and infers an effective pointcut expression for each cluster of join points. The approach also provides an additional testing mechanism to ensure that the inferred pointcut expressions are of correct strength. The empirical results show that our approach helps achieve a significant reduction in the total number of pointcut expressions to be used in the refactored code.