We present a value profile based approach for ranking program statements according to their likelihood of being faulty. The key idea is to see which program statements exercised during a failing run use values that can be altered so that the execution instead produces correct output. Our approach is effective in locating statements that are either faulty or directly linked to a faulty statement. We present experimental results showing the effectiveness and efficiency of our approach. Our approach outperforms Tarantula [9] which, to our knowledge, is the most effective prior approach for statement ranking based fault localization using the benchmark programs we studied.
Abstract-Software testing is a critical part of software development. As new test cases are generated over time due to software modifications, test suite sizes may grow significantly. Because of time and resource constraints for testing, test suite minimization techniques are needed to remove those test cases from a suite that, due to code modifications over time, have become redundant with respect to the coverage of testing requirements for which they were generated. Prior work has shown that test suite minimization with respect to a given testing criterion can significantly diminish the fault detection effectiveness (FDE) of suites. We present a new approach for test suite reduction that attempts to use additional coverage information of test cases to selectively keep some additional test cases in the reduced suites that are redundant with respect to the testing criteria used for suite minimization, with the goal of improving the FDE retention of the reduced suites. We implemented our approach by modifying an existing heuristic for test suite minimization. Our experiments show that our approach can significantly improve the FDE of reduced test suites without severely affecting the extent of suite size reduction.
Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible. The sizes of test suites grow as software evolves. Due to resource constraints, it is important to prioritize the execution of test cases so as to increase chances of early detection of faults. Prior techniques for test case prioritization are based on the total number of coverage requirements exercised by the test cases. In this paper, we present a new approach to prioritize test cases based on the coverage requirements present in the relevant slices of the outputs of test cases. We present experimental results comparing the effectiveness of our prioritization approach with that of existing techniques that only account for total requirement coverage, in terms of ability to achieve high rate of fault detection. Our results present interesting insights into the effectiveness of using relevant slices for test case prioritization.
We present a tool called BugFix that can assist developers in fixing program bugs. Our tool automatically analyzes the debugging situation at a statement and reports a prioritized list of relevant bug-fix suggestions that are likely to guide the developer to an appropriate fix at that statement. BugFix incorporates ideas from machine learning to automatically learn from new debugging situations and bug fixes over time. This enables more effective prediction of the most relevant bug-fix suggestions for newlyencountered debugging situations. The tool takes into account the static structure of a statement, the dynamic values used at that statement by both passing and failing runs, and the interesting value mapping pairs [17] associated with that statement. We present a case study illustrating the efficacy of BugFix in helping developers to fix bugs.
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