A review of slicing techniques in software engineering

Program slice is the part of program that may take the program off the path of the desired output at some point of its execution. Such point is known as the slicing criterion. This point is generally identified at a location in a given program coupled with the subset of variables of program. This process in which program slices are computed is called program slicing. Weiser was the person who gave the original definition of program slice in 1979. Since its first definition, many ideas related to the program slice have been formulated along with the numerous numbers of techniques to compute program slice. Meanwhile, distinction between the static slice and dynamic slice was also made. Program slicing is now among the most useful techniques that can fetch the particular elements of a program which are related to a particular computation. Quite a large numbers of variants for the program slicing have been analyzed along with the algorithms to compute the slice. Model based slicing split the large architectures of software into smaller sub models during early stages of SDLC. Software testing is regarded as an activity to evaluate the functionality and features of a system. It verifies whether the system is meeting the requirement or not. A common practice now is to extract the sub models out of the giant models based upon the slicing criteria. Process of model based slicing is utilized to extract the desired lump out of slice diagram. This specific survey focuses on slicing techniques in the fields of numerous programing paradigms like web applications, object oriented, and components based. Owing to the efforts of various researchers, this technique has been extended to numerous other platforms that include debugging of program, program integration and analysis, testing and maintenance of software, reengineering, and reverse engineering. This survey portrays on the role of model based slicing and various techniques that are being taken on to compute the slices

Combining Static and Dynamic Analysis for Vulnerability Detection

In this paper, we present a hybrid approach for buffer overflow detection in C code. The approach makes use of static and dynamic analysis of the application under investigation. The static part consists in calculating taint dependency sequences (TDS) between user controlled inputs and vulnerable statements. This process is akin to program slice of interest to calculate tainted data- and control-flow path which exhibits the dependence between tainted program inputs and vulnerable statements in the code. The dynamic part consists of executing the program along TDSs to trigger the vulnerability by generating suitable inputs. We use genetic algorithm to generate inputs. We propose a fitness function that approximates the program behavior (control flow) based on the frequencies of the statements along TDSs. This runtime aspect makes the approach faster and accurate. We provide experimental results on the Verisec benchmark to validate our approach.Comment: There are 15 pages with 1 figur

Stop-list slicing.

Traditional program slicing requires two parameters: a program location and a variable, or perhaps a set of variables, of interest. Stop-list slicing adds a third parameter to the slicing criterion: those variables that are not of interest. This third parameter is called the stoplist. When a variable in the stop-list is encountered, the data-flow dependence analysis of slicing is terminated for that variable. Stop-list slicing further focuses on the computation of interest, while ignoring computations known or determined to be uninteresting. This has the potential to reduce slice size when compared to traditional forms of slicing. In order to assess the size of the reduction obtained via stop-list slicing, the paper reports the results of three empirical evaluations: a large scale empirical study into the maximum slice size reduction that can be achieved when all program variables are on the stop-list; a study on a real program, to determine the reductions that could be obtained in a typical application; and qualitative case-based studies to illustrate stop-list slicing in the small. The large-scale study concerned a suite of 42 programs of approximately 800KLoc in total. Over 600K slices were computed. Using the maximal stoplist reduced the size of the computed slices by about one third on average. The typical program showed a slice size reduction of about one-quarter. The casebased studies indicate that the comprehension effects are worth further consideration

Algorithmic Debugging of Real-World Haskell Programs: Deriving Dependencies from the Cost Centre Stack

Existing algorithmic debuggers for Haskell require a transformation of all modules in a program, even libraries that the user does not want to debug and which may use language features not supported by the debugger. This is a pity, because a promising ap- proach to debugging is therefore not applicable to many real-world programs. We use the cost centre stack from the Glasgow Haskell Compiler profiling environment together with runtime value observations as provided by the Haskell Object Observation Debugger (HOOD) to collect enough information for algorithmic debugging. Program annotations are in suspected modules only. With this technique algorithmic debugging is applicable to a much larger set of Haskell programs. This demonstrates that for functional languages in general a simple stack trace extension is useful to support tasks such as profiling and debugging

Safe Concurrency Introduction through Slicing

Traditional refactoring is about modifying the structure of existing code without changing its behaviour, but with the aim of making code easier to understand, modify, or reuse. In this paper, we introduce three novel refactorings for retrofitting concurrency to Erlang applications, and demonstrate how the use of program slicing makes the automation of these refactorings possible