Concurrent computer programs are fast becoming prevalent in many critical applications. Unfortunately, these programs are especially difficult to test and debug. Recently, it has been suggested that injecting random timing noise into many points within a program can assist in eliciting bugs within the program. Upon eliciting the bug, it is necessary to identify a minimal set of points that indicate the source of the bug to the programmer. In this paper, we pose this problem as an active feature selection problem. We propose an algorithm called the iterative group sampling algorithm that iteratively samples a lower dimensional projection of the program space and identifies candidate relevant points. We analyze the convergence properties of this algorithm. We test the proposed algorithm on several real-world programs and show its superior performance. Finally, we show the algorithms' performance on a large concurrent program.