More Debugging in Parallel

Programs may contain multiple faults, in which case their debugging can be parallelized. However, effective parallelization requires some guarantees that parallel debugging tasks do not address the same fault, an inherent problem of earlier, clustering-based approaches to parallel debugging. In this paper, we identify a number of fundamental trade-offs to be made when selecting algorithms for parallel debugging, and explore these trade-offs using one clustering algorithm and three algorithms from integer linear programming. Results of an evaluation involving a total of 75,000 faulty versions (with up to 32 injected faults) of 15 subject programs suggest that depending on the number of faults present and the trade-offs one is willing to accept, speed-ups much larger than previously reported can be achieved, even if all derived parallel debugging tasks are handled sequentially.