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Find a Research Topic from Real Problems/Challenges You Encounter

Find a Research Topic from Real Problems/Challenges You Encounter. Fault diagnosis: manual and inefficient 15-20 reports per person day [Microsoft06] 120 lines of code per person hour [Hatton08] Reports from CVE This buffer overflow … related to the data race at …

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Find a Research Topic from Real Problems/Challenges You Encounter

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  1. Find a Research Topic from Real Problems/Challenges You Encounter • Fault diagnosis: manual and inefficient • 15-20 reports per person day [Microsoft06] • 120 lines of codeper person hour [Hatton08] • Reports from CVE • This buffer overflow … related to the data race at … • Null pointer dereference …. and integer bugs

  2. Types of Correlated Faults Found in CVE *:unique correlation; ×: not unique;#: correlated with the same fault

  3. Research Problem: how we automatically computer such information ? [FSE10]

  4. Find Research Problems from Literature Our future work includes extending the techniques to handle pointers and recursive calls and to further improve the code partition strategies.

  5. Importance of the Problem Since its first application in debugging in 1969 [1], [2], symbolic analysis has demonstrated its break-through capabilities in bug finding and test input generation [3], [4], [5], [6], [7]. Compared to dynamic testing, symbolic analysis explores program behaviors along as many execution paths as possible and is potentially able to reason for paths which randomly generated input cannot reach. Besides its coverage, symbolic analysis requires no executables and can be applied early in software lifecycle while fixing a bug is cheaper. Compared to traditional static analysis such as abstract interpretation, symbolic analysis tracks actual symbolic values and relations of variables at program points along program paths and is able to provide more precise information. Recent research has shown that by running symbolic analysis in parallel on clusters, we can make it applicable for complex software systems [8]. With such potential of scalability, symbolic analysis will continue being a powerful tool for extracting program semantic information for important software engineering tools.

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