On the Preliminary Adaptive Random Testing of Aspect-Oriented Programs

Adaptive random testing (ART) is a new family of random-based test data generation and selection strategies that enhances the effectiveness of tests over the classical random testing (RT). ART has been widely investigated and studied in numerous research papers over the recent years. These studies have included proposing various techniques for implementing and improving the intuition behind ART (evenly spread of test cases over the input domain, measured by some distance measures) generally for procedural programs with numerical input domain and most recently object-oriented programs. However, there is currently no work available in the literature that discusses the applicability of ART to aspect-oriented programming (AOP), as it is gaining popularity in software development. Inspired by this, this paper aims to investigate the possible ways that ART can be applied to AOP. This investigation focuses on a multi-perspective analysis of the current ART-based techniques. In this respect, we identified three related perspectives based on the current state of art in the area of ART. Each perspective was analyzed in terms of its applicability and possibility for aspect-oriented programs, particularly its constituent distance measure. As a result, our study gives rise to some interesting points and outlines a number of potential research directions in applying ART to AOP. This can pave the way for efficient development on applying of ART to AOP and finally AOP success.