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Static program slicing is a technique to detect the program parts (i.e. the “slice”) of the given program possibly affecting a given property. The technique is of interest for analysing safety-critical software, since it can identify the program parts that may affect various safety properties. Verification efforts can then be directed towards those parts, leading to a more efficient verification process. We have developed a novel method for static backward program slicing. The method works for well-structured programs, as commonly demanded by coding standards for safety-critical software. It utilises the program structure to obtain a highly efficient slicing process, where control dependencies are inferred from the program structure, and the slicing is done on-the-fly concurrently with the data dependence analysis. We have evaluated our method experimentally. For applications that require few slices to be taken, like checking for a set of safety properties, we obtain large speedups as compared with the standard method for static backward program slicing. We have also investigated how the speedup varies with various parameters such as code size, size of the slice relative to the full program, and relative frequency of conditions in the code.

Backward program slicing identifies the program parts that might influence a particular variable at a program point. A program part (e.g., a statement) can be directly influenced by another part due to its data or control dependence on the later. The classical program slicing approaches are designed to find in advance all the data and control dependencies in the program. This design entails a considerable amount of unnecessary computations because not all the dependencies are required for computing the slice. Demand-driven program slicing approaches try to raise the analysis performance by avoiding the unnecessary computations. However, these approaches cannot address unstructured programs in a demand-driven fashion. On the other hand, the existing techniques that compute the control dependencies in unstructured programs are based on fixed-point iterations, which limits their integration to the demand-driven slicing approaches. Program slicing based on Predicate Code Block (PCB) is a new demand-driven slicing approach that can address only structured programs. This paper presents the first demand-driven technique to compute the control dependencies in unstructured programs. In this regard, the technique uses flow information, location-based information and syntactic structure of the source code. Further, the paper shows how the new technique can be integrated to the PCB-based slicing approach to address unstructured programs.