Static Backward Demand-Driven Slicing

Program slicing identifies the program parts that may affect certain properties of the program, such as the outcomes of conditions affecting the program flow. Ottenstein's Program Dependence Graph (PDG) based algorithm is the state-of-practice for static slicing today: it is well-suited in applications where many slices are computed, since the cost of building the PDG then can be amortized over the slices. But there are applications that require few slices of a given program, and where computing all the dependencies may be unnecessary. We present a light-weight interprocedural algorithm for backward static slicing where the data dependence analysis is done using a variant of the Strongly Live Variables (SLV) analysis. This allows us to avoid building the Data Dependence Graph, and to slice program statements "on-the-fly'' during the SLV analysis which is potentially faster for computing few slices. Furthermore we use an abstract interpretation-based value analysis to extend our slicing algorithm to slice low-level code, where data dependencies are not evident due to dynamically calculated addresses. Our algorithm computes slices as sets of Control Flow Graph nodes: we show how to adapt existing techniques to generate executable slices that correspond to semantically correct code, where jump statements have been inserted at appropriate places. We have implemented our slicing algorithms, and made an experimental evaluation comparing them with the standard PDG-based algorithm for a number of example programs. We obtain the same accuracy as for PDG-based slicing, sometimes with substantial improvements in performance.