Collapsible Tabular Visualization of Aspects in Object Oriented Programming

Due to its implicit invocation nature ofAspect Oriented Programming (AOP), locating a jointpoint for executing aspect is extremely difficult. Hence,it becomes difficult to understand the application’sflow and behavior. Current AOP visualization tools havelimitations such as high dependency on other tools,confusing and excessive use of color to represent aspectsand using an outdated version of AspectJ. In thispaper, we propose a new approach collapsibletabular visualization tool to visualize and represent AOPfeatures to aid the programmers in betterunderstanding AOP applications. We have come outof traditional color-based aspect visualization and developeda web based tool: AspectViz that visualizes the aspects ina simple collapsible table. A questionnaire containingfour different questions related to aspects visualizationwas developed to compare AspectViz with currentvisualization tools. 20 graduate students andprofessional software developers were invited to participatein the test experiment as well as the survey. We havecompared its performance with existing AOPvisualization tools i.e. the AJDT and the AspectMaps andshowed how it outperformed in many cases, which is nocolor confusion, simple tabular visualization of aspects, nodependency on third-party software, easy to understand andthe time it took to find a particular aspect was less etc.Collapsible tabular visualization enhanced the usability andperformance of aspect locating in aspect-orientedprogramming

Metrics for Aspect Mining Visualization

Aspect oriented programming has over the last decade become the subject of intense research within the domain of software engineering. Aspect mining, which is concerned with identification of cross cutting concerns in legacy software, is an important part of this domain. Aspect refactoring takes the identified cross cutting concerns and converts these into new software constructs called aspects. Software that have been transformed using this process becomes more modularized and easier to comprehend and maintain. The first attempts at mining for aspects were dominated by manual searching and parsing through source code using simple tools. More sophisticated techniques have since emerged including evaluation of execution traces, code clone detection, program slicing, dynamic analysis, and use of various clustering techniques. The focus of most studies has been to maximize aspect mining performance measured by various metrics including those of aspect mining precision and recall. Other metrics have been developed and used to compare the various aspect mining techniques with each other. Aspect mining automation and presentation of aspect mining results has received less attention. Automation of aspect mining and presentation of results conducive to aspect refactoring is important if this research is going to be helpful to software developers. This research showed that aspect mining can be automated. A tool was developed which performed automated aspect mining and visualization of identified cross cutting concerns. This research took a different approach to aspect mining than most aspect mining research by recognizing that many different categories of cross cutting concerns exist and by taking this into account in the mining process. Many different aspect mining techniques have been developed over time, some of which are complementary. This study was different than most aspect mining research in that multiple complementary aspect mining algorithms was used in the aspect mining and visualization process