Application of an Effective Fault Localization Prioritization Method to Stereo Matching Software

During the process of software development, there will always exist some faults. At this time, we need to locate the specific faults and fix them. Fault Localization is a complicated and time-consuming process. This paper utilizes the executed test cases to establish the prediction model of test case execution results through neural network and sorts the unexecuted test cases according to the coverage and prediction results. Executes the test cases according to this order and records the execution status. Substitutes the execution information into the formula of Tarantula and calculates the suspiciousness values of program elements. Developers check program elements in non-increasing order of suspiciousness values until a fault is found. Finally, the method proposed in this paper is applied to stereo matching software, and it is found that the efficiency is higher than that of random method.

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