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2008 23rd IEEE/ACM International Conference on Automated Software Engineering (2008)
Sept. 15, 2008 to Sept. 19, 2008
ISBN: 978-1-4244-2187-9
pp: 307-316
E. Yom-Tov , IBM, Haifa Univ. Campus, Haifa
R. Tzoref , IBM, Haifa Univ. Campus, Haifa
S. Ur , IBM, Haifa Univ. Campus, Haifa
S. Hoory , IBM, Haifa Univ. Campus, Haifa
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.
real-world programs, automatic debugging, concurrent programs, low dimensional random projections, concurrent computer programs, random timing noise, iterative group sampling algorithm

R. Tzoref, E. Yom-Tov, S. Hoory and S. Ur, "Automatic Debugging of Concurrent Programs through Active Sampling of Low Dimensional Random Projections," 2008 23rd IEEE/ACM International Conference on Automated Software Engineering(ASE), L'Aquila, 2008, pp. 307-316.
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