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We previously presented a fault localization technique called value replacement that repeatedly alters the state of an executing program to locate a faulty statement [9]. The technique searches for program statements involving values that can be altered during runtime to cause the incorrect output of a failing run to become correct. We showed that highly effective fault localization results could...
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...
Prior work has shown that computing dynamic slices of erroneous program values can greatly assist in locating the root cause of erroneous behavior by identifying faulty statements in sequential programs. These dynamic slices represent backward transitive closure over exercised read-after-write data dependences and control dependences. However, for a multithreaded program executing on a processor,...
We present a general approach for automatically isolating the root causes of memory-related bugs in software. Our approach is based on the observation that most memory bugs involve uses of corrupted memory locations. By iteratively suppressing (nullifying) the effects of these corrupted memory locations during program execution, our approach gradually isolates the root cause of a memory bug. Our approach...
We present an online framework to capture and recover from program failures and prevent them from occurring in the future through safe execution perturbations. The perturbations are safe as they respect the semantics of the program. We use a checkpointing/logging mechanism to capture a program execution to an event log. If the execution results in a failure, the framework automatically searches for...
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