Poster: Efficiently finding minimal failing input in MapReduce programs

Debugging of distributed computing model programs like MapReduce is a difficult task. That's why prior studies only focus on finding and fixing bugs in early stages of program development. Delta debugging tries to find minimal failing input in sequential programs by dividing inputs into subsets and testing these subsets one-by-one. But no prior work tries to find minimal failing input in distributed programs like MapReduce. In this paper, we present MapRedDD, a framework to efficiently find minimal failing input in MapReduce programs. MapRedDD employs failing input selection technique, focused on identifying the failing input subset in the single run of MapReduce program with multiple input subsets instead of testing each subset separately. This helps to reduce the number of executions of MapReduce program for each input subset and overcome the overhead of job submission, job scheduling and final outcome retrieval. Our work can efficiently find the minimal failing input in the number of executions equal to the number of inputs to MapReduce program N as opposed to the number of executions of MapReduce program equal to the number of input subsets 2^N-1 in worst case for binary search invariant algorithm to find minimal failing input.