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Reconsiders two common assumptions about how software should be analyzed
Arrives at some striking new results
Presents an algorithm for isolating multiple bugs from sparsely sampled data taken from many thousands of program executions
This monograph reconsiders two common assumptions about how software should be analyzed and arrives at some striking new results. This new approach utilizes some tools used by biologists and economists to understand complicated systems by considering programs as statistical processes and using statistical techniques to understand software.
It presents an algorithm for isolating multiple bugs from sparsely sampled data taken from many thousands of program executions. This algorithm has unique properties that complement other program analysis techniques; in particular, it is potentially able to find the root cause of any program failure without first requiring an explicit specification of the property to check. Ben Liblit presents a new and fundamental approach to software analysis that will provide a source of ideas and inspiration to the field for many years to come.
This monograph constitutes a thoroughly revised and extended version of the author's PhD thesis, which was selected as the winning thesis of the 2005 ACM Doctoral Dissertation Competition. Ben Liblit did his PhD work at the University of California, Berkeley, with Alexander Aiken as thesis adviser.