Dynamic Slice of Aspect Oriented Program A Comparative Study

Aspect Oriented Programming (AOP) is a budding latest technology for separating crosscutting concerns . It is very difficult to achieve cross cutting concerns in object - oriented programming (OOP). AOP is generally suitable for the area where code scattering and code tangling arises. Due to the specific features of AOP language such as joinpoint, point - cut, advice and introduction, it is difficult to apply existing slicing algorithms of procedural or object - oriented programming directly to AOP. This paper addresses different types of program slicing approaches for AOP by considering a very simple example. Also this paper addresses a new approach to calculate the dynamic slice of AOP. The complexity of this algorithm is better as compared to some existing algorithms

Static Slicing of Interprocedural Programs

Program slicing has many applications in a software development environment such as debugging, testing, anomaly detection, program understanding and many more. The concept being introduced by Weiser and it was started with static slicing calculation. Talking about static slicing, it is a subset of statements of a program which directly or indirectly affect the values of the variables computed providing a slicing criterion. In this project, we have developed an approach for creating an intermediate representation of a program in the form of System Dependence Graph (SDG) which is to be, again taken as input for computing the slicing of a program with respect to slicing criterion. The slicing approach computes the slices with respect to a given slicing criterion. For generating the graph, we have analysed the input program, identified the tokens and finally generated the relation between tokens as data dependent or control dependent. For calculating the slice, we have used two-phase graph reachability algorithm developed by Horwitz, Reps and Binkley, which creates a graph consisting of only those nodes that are dependent on slicing criterion. Finally we have plotted a graph between time taken to create graph versus number of functions used in program. Our approach of calculating slices has been limited only to C programs

Dependence Communities in Source Code

Dependence between components in natural systems is a well studied phenomenon in the form of biological and social networks. The concept of community structure arises from the analysis of social networks and has successfully been applied to complex networks in other fields such as biology, physics and computing. We provide empirical evidence that dependence between statements in source code gives rise to community structure. This leads to the introduction of the concept of dependence communities in software and we provide evidence that they reflect the semantic concerns of a program. Current definitions of sliced-based cohesion and coupling metrics are not defined for procedures which do not have clearly defined output variables and definitions of output variable vary from study-to-study. We solve these problems by introducing corresponding new, more efficient forms of slice-based metrics in terms of maximal slices. We show that there is a strong correlation between these new metrics and the old metrics computed using output variables. We conduct an investigation into dependence clusters which are closely related to dependence communities. We undertake an empirical study using definitions of dependence clusters from previous studies and show that, while programs do contain large dependence clusters, over 75% of these are not ‘true’ dependence clusters. We bring together the main elements of the thesis in a study of software quality, investigating their interrelated nature. We show that procedures that are members of multiple communities have a low cohesion, programs with higher coupling have larger dependence communities, programs with large dependence clusters also have large dependence communities and programs with high modularity have low coupling. Dependence communities and maximal-slice-based metrics have a huge number of potential applications including program comprehension, maintenance, debugging, refactoring, testing and software protection