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Locating Software Faults Based on Minimum Debugging Frontier Set

机译:基于最小调试边界集的软件故障定位

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In this article, we propose a novel state-based fault-localization approach. Given an observed failure that is reproducible under the same program input, this new approach uses two main techniques to reduce the state exploration cost. Firstly, the execution trace to be analyzed for the observed failure is successively narrowed by making the set of trace points in each step a cut of the dynamic dependence graph. Such a cut divides the remaining trace into two parts and, based on the sparse symbolic exploration outcome, one part is removed from further exploration. This process continues until reaching where the fault is determined to be. Second, the cut in each step is chosen such that the union of the program states from the members of the cut is of the minimum size among all candidate cuts. The set of statement instances in the chosen cut is called a minimum debugging frontier set (MDFS). To evaluate our approach, we apply it to 16 real bugs from real world programs and compare our fault reports with those generated by state-of-the-art approaches. Results show that the MDFS approach obtains high quality fault reports for these test cases with considerably higher efficiency than previous approaches.
机译:在本文中,我们提出了一种新颖的基于状态的故障定位方法。鉴于观察到的故障在相同程序输入下可重现,因此该新方法使用两种主要技术来降低状态勘探成本。首先,通过使每个步骤中的跟踪点集成为动态相关图的一部分,来逐渐缩小要分析观察到的故障的执行轨迹。这样的切割将剩余的痕迹分为两部分,并基于稀疏的象征性勘探结果,从进一步的勘探中删除了一部分。这个过程一直持续到确定故障所在。其次,选择每个步骤中的剪切,使得来自剪切成员的程序状态的并集在所有候选剪切中均具有最小大小。所选剪切中的语句实例集称为最小调试边界集(MDFS)。为了评估我们的方法,我们将其应用于实际程序中的16个实际错误,并将我们的故障报告与最新方法所生成的错误报告进行比较。结果表明,与以前的方法相比,MDFS方法可以为这些测试案例获得高质量的故障报告,效率要高得多。

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