A graphical tool for observing state and behavioral changes at join points

To comprehend programs or to fix a bug, programmers always mentally simulate the program execution by reading the source code. Aspect-oriented programming (AOP) increases this mental effort, because it can alter the state and the behavior of the base program at a join point to any extent by executing advices. Advices are implicitly invoked in the source code and their compositions at a join point may vary according to the runtime context. They can access and even change the context values of join points. Without appropriate tools, it is difficult to notice the effects of the implicitly executed advices. The goal of my work is to increase the comprehensibility of AO programs by using a graphical tool, that can succinctly visualize the state and behavioral changes at join points

Locating Potential Aspect Interference Using Clustering Analysis

Software design continues to evolve from the structured programming paradigm of the 1970s and 1980s and the object-oriented programming (OOP) paradigm of the 1980s and 1990s. The functional decomposition design methodology used in these paradigms reduced the prominence of non-functional requirements, which resulted in scattered and tangled code to address non-functional elements. Aspect-oriented programming (AOP) allowed the removal of crosscutting concerns scattered throughout class code into single modules known as aspects. Aspectization resulted in increased modularity in class code, but introduced new types of problems that did not exist in OOP. One such problem was aspect interference, in which aspects meddled with the data flow or control flow of a program. Research has developed various solutions for detecting and addressing aspect interference using formal design and specification methods, and by programming techniques that specify aspect precedence. Such explicit specifications required practitioners to have a complete understanding of possible aspect interference in an AOP system under development. However, as system size increased, understanding of possible aspect interference could decrease. Therefore, practitioners needed a way to increase their understanding of possible aspect interference within a program. This study used clustering analysis to locate potential aspect interference within an aspect-oriented program under development, using k-means partitional clustering. Vector space models, using two newly defined metrics, interference potential (IP) and interference causality potential (ICP), and an existing metric, coupling on advice execution (CAE), provided input to the clustering algorithms. Resulting clusters were analyzed via an internal strategy using the R-Squared, Dunn, Davies-Bouldin, and SD indexes. The process was evaluated on both a smaller scale AOP system (AspectTetris), and a larger scale AOP system (AJHotDraw). By seeding potential interference problems into these programs and comparing results using visualizations, this study found that clustering analysis provided a viable way for detecting interference problems in aspect-oriented software. The ICP model was best at detecting interference problems, while the IP model produced results that were more sporadic. The CAE clustering models were not effective in pinpointing potential aspect interference problems. This was the first known study to use clustering analysis techniques specifically for locating aspect interference