Exploiting the empirical characteristics of program dependences for improved forward computation of dynamic slices

Forward computing algorithms for dynamic slicing operate in tandem with program execution and generally do not require a previously stored execution trace, which make them suitable for interactive debugging and online analysis of long running programs. Both the time and space requirements of such algorithms are generally high due to the fact that they compute and maintain in memory the dynamic slices associated with all variables defined during execution. In this paper we empirically identify several characteristics of program dependences that we exploit to develop a memoization-based forward computing dynamic slicing algorithm whose runtime cost is better than that of any existing algorithm in its class. We also conduct an empirical comparative study contrasting the performance of our new algorithm to the performance of four other algorithms. One is a well known basic algorithm, and the remaining three, use reduced ordered binary decision diagrams (roBDDs) to maintain dynamic slices. Our results indicate that the new memoization-based algorithm is: (1) considerably more time and space efficient than the basic algorithm and one of the roBDD-based algorithms designed to be suitable for online analysis; and (2) comparable in terms of time efficiency but consistently more space efficient than the remaining two roBDD-based algorithms.