A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
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ACM Transactions on Computer Systems (TOCS)
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SPDT '98 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
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SPDT '98 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
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ISOLA '06 Proceedings of the Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation
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IBM Systems Journal
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CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
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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.