AspectMaps: A Scalable Visualization of Join Point Shadows

International audienceWhen using Aspect-Oriented Programming, it is sometimes difficult to determine at which join point an aspect executes. Similarly, when considering one join point, knowing which aspects will execute there and in what order is non- trivial. This makes it difficult to understand how the application will behave. A number of visualizations have been proposed that attempt to provide support for such program understanding. However, they neither scale up to large code bases nor scale down to understanding what happens at a single join point. In this paper, we present AspectMaps - a visualization that scales in both directions, thanks to a multi-level selective structural zoom. We show how the use of AspectMaps allows for program understanding of code with aspects, revealing both a wealth of information of what can happen at one particular join point as well as allowing to see the "big picture" on a larger code base. We demonstrate the usefulness of AspectMaps on an example and present the results of a small user study that shows that AspectMaps outperforms other aspect visualization tools

Mango: A model-driven approach to engineering green Mobile Cloud Applications

With the resource constrained nature of mobile devices and the resource abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource/computation intensive tasks from mobile devices to the cloud. Most of the existing offloading techniques can only be applied to legacy mobile applications as they are motivated by existing systems. Consequently, they are realised with custom runtimes which incur overhead on the application. Moreover, existing approaches which can be applied to the software development phase, are difficult to implement (based on manual process) and also fall short of overall (mobile to cloud) efficiency in software qualityattributes or awareness of full-tier (mobile to cloud) implications.To address the above issues, the thesis proposes a model-driven architecturefor integration of software quality with green optimisation in Mobile Cloud Applications (MCAs), abbreviated as Mango architecture. The core aim of the architecture is to present an approach which easily integrates software quality attributes (SQAs) with the green optimisation objective of Mobile Cloud Computing (MCC). Also, as MCA is an application domain which spans through the mobile and cloud tiers; the Mango architecture, therefore, takesinto account the specification of SQAs across the mobile and cloud tiers, for overall efficiency. Furthermore, as a model-driven architecture, models can be built for computation intensive tasks and their SQAs, which in turn drives the development – for development efficiency. Thus, a modelling framework (called Mosaic) and a full-tier test framework (called Beftigre) were proposed to automate the architecture derivation and demonstrate the efficiency of Mango approach. By use of real world scenarios/applications, Mango has been demonstrated to enhance the MCA development process while achieving overall efficiency in terms of SQAs (including mobile performance and energy usage compared to existing counterparts)

Co-Evolution of Source Code and the Build System: Impact on the Introduction of AOSD in Legacy Systems

Software is omnipresent in our daily lives. As users demand ever more advanced features, software systems have to keep on evolving. In practice, this means that software developers need to adapt the description of a software application. Such a description not only consists of source code written down in a programming language, as a lot of knowledge is hidden in lesser known software development artifacts, like the build system. As its name suggests, the build system is responsible for building an executable program, ready for use, from the source code. There are various indications that the evolution of source code is strongly related to that of the build system. When the source code changes, the build system has to co-evolve to safeguard the ability to build an executable program. A rigid build system on the other hand limits software developers. This phenomenon especially surfaces when drastic changes in the source code are coupled with an inflexible build system, as is the case for the introduction of AOSD technology in legacy systems. AOSD is a young software development approach which enables developers to structure and compose source code in a better way. Legacy systems are old software systems which are still mission-critical, but of which the source code and the build system are no longer fully understood, and which typically make use of old(-fashioned) technology. This PhD dissertation focuses on finding an explanation for this co-evolution of source code and the build system, and on finding developer support to grasp and manage this phenomenon. We postulate four "roots of co-evolution" which represent four different ways in which source code and the build system interact with each other. Based on these roots, we have developed tool and aspect language support to understand and manage co-evolution. The roots and the tool support have been validated in case studies, both in the context of co-evolution in general and of the introduction of AOSD technology in legacy systems. The dissertation experimentally shows that co-evolution indeed is a real problem, but that specific software development and aspect language support enables developers to deal with it

A meta-language and framework for aspect-oriented programming

Tese de mestrado integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia. 201

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