A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest, referred to as a slicing criterion. The task of computing program slices is called program slicing. The original definition of a program slice was presented by Weiser in 1979. Since then, various slightly different notions of program slices have been proposed, as well as a number of methods to compute them. An important distinction is that between a static and a dynamic slice. The former notion is computed without making assumptions regarding a program's input, whereas the latter relies on some specific test case. Procedures, arbitrary control flow, composite datatypes and pointers, and interprocess communication each require a specific solution. We classify static and dynamic slicing methods for each of these features, and compare their accuracy and efficiency. Moreover, the possibilities for combining solutions for different features are investigated....

The first interprocedural modification side-effects analysis for C (MOD_C) that obtains better than worst-case precision on programs with general-purpose pointer usage is presented with empirical results. The analysis consists of an algorithm schema corresponding to a family of MODC algorithms with two independent phases: one for determining pointer-induced aliases and a subsequent one for propagating interprocedural side effects. These MOD_C algorithms are parameterized by the aliasing method used. The empirical results compare the performance of two dissimilar MOD_C algorithms: MOD_C(FSAlias) uses a flow-sensitive, calling-context-sensitive interprocedural alias analysis [LR92]; MOD_C(FIAlias) uses a flow-insensitive, calling-context-insensitive alias analysis which is much faster, but less accurate. These two algorithms were profiled on 45 programs ranging in size from 250 to 30,000 lines of C code, and the results demonstrate dramatically the possible cost-precision tradeoffs. This first comparative implementation of MODC analyses offers insight into the differences between flow-/context-sensitive and flow-/context-insensitive analyses. The analysis cost versus precision tradeoffs in side-effect information obtained is reported. The results show surprisingly that the precision of flow-sensitive side-effect analysis is not always prohibitive in cost, and that the precision of flow-insensitive analysis is substantially better than worst-case estimates and seems sufficient for certain applications. On average MODC (FSAlias) for procedures and calls is in the range of 20% more precise than MODC (F IAlias); however, the performance was found to be at least an order of magnitude slower than MODC (F IAlias).

Program slicing is a decomposition technique that elides program components not relevant to a chosen computation, referred to as a slicing criterion. The remaining components form an executable program called a slice that computes a projection of the original program’s semantics. Using examples coupled with fundamental principles, a tutorial introduction to program slicing is presented. Then applications of program slicing are surveyed, ranging from its first use as a debugging technique to current applications in property verification using finite state models. Finally, a summary of research challenges for the slicing community is discussed.

Traditional, syntax-preserving program slicing simpli es a program by deleting components (e.g., statements and predicates) that do not aect a computation of interest. Amorphous slicing removes the limitation to component deletion as the only means of simpli cation, while retaining the semantic property that a slice preserves the selected behaviour of interest from the original program. This leads to slices which are often considerably smaller than their syntax-preserving counterparts.

this paper we present a debugging model, based on dynamic program slicing and execution backtracking techniques, that easily lends itself to automation. This model is based on experience with using these techniques to debug software. We also present a prototype debugging tool, SPYDER, that explicitly supports the proposed model, and with which we are performing further debugging research

While mutation testing has proved to be an effective way of finding software faults, currently it is only applied to relatively small programs. One of the main reasons for this is the human analysis required in detecting equivalent mutants. Here program slicing is used to simplify this problem. Program slicing is also used to reduce the number of equivalent mutants produced.

Program slices have potential uses in many software engineering applications. Traditional slicing algorithms, however, do not work correctly on programs that contain explicit jump statements. Two similar algorithms were proposed recently to alleviate this problem. Both require the flowgraph and the program dependence graph of the program to be modified. In this paper, we propose an alternative algorithm that leaves these graphs intact and uses a separate graph to store the additional required information. We also show that this algorithm permits an extremely efficient, conservative adaptation for use with programs that contain only "structured" jump statements.

Program slicing is a technique for automatically identifying all the lines in a program which affect a selected subset of variables. A large program can be divided into a number of smaller programs (its slices), each constructed for different variable subsets. The slices are typically simpler than the original program, thereby simplifying the process of testing a property of the program which only concerns a subset of its variables. Some aspects of a program's computation are not captured by a set of variables, rendering slicing inapplicable. To overcome this difficulty we make a program introspective, adding assignments to denote these `implicit' computations. Initially this makes the program longer. However, slicing can now be applied to the introspective program, forming a slice concerned solely with the implicit computation. We improve the simplification power of slicing using program transformation. To illustrate our approach we consider the implicit computation which ...

Program slicing is a viable method to restrict the focus of a task to specific sub-components of a program. Examples of applications include debugging, testing, program comprehension, restructuring, downsizing, and parallelization. This paper discusses different statement deletion based slicing methods, together with algorithms and applications to software engineering.

We present TCEL (Time-Constrained Event Language), whose timing semantics is based solely on the constrained relationships between observable events. Using this semantics, the unobservable code can be automatically moved to convert an unschedulable task set into a schedulable one. We illustrate this by an application of program-slicing, which we use to automatically tune control-domain systems driven by ratemonotonic scheduling.