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Cooperative bug isolation : winning thesis of the 2005 ACM Doctoral Dissertation Competition

Author: Ben Liblit
Publisher: Berlin : Springer-Verlag, ©2007.
Dissertation: Revised thesis (Ph. D.)--University of California, Berkeley, 2004.
Series: Lecture notes in computer science, 4440.; ACM distinguished theses.; LNCS sublibrary., SL 2,, Programming and software engineering.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : EnglishView all editions and formats
Summary:
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. This monograph reconsiders two common assumptions about how we should analyze software and arrives at some striking new  Read more...
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Details

Genre/Form: Academic theses
Additional Physical Format: Print version:
Liblit, Ben.
Cooperative bug isolation.
Berlin : Springer-Verlag, ©2007
(DLC) 2007924082
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ben Liblit
ISBN: 354071877X 9783540718772
OCLC Number: 1005816582
Description: 1 online resource (xv, 101 pages) : illustrations
Contents: Instrumentation Framework --
Practical Considerations --
Techniques for Statistical Debugging --
Related Work --
Conclusion.
Series Title: Lecture notes in computer science, 4440.; ACM distinguished theses.; LNCS sublibrary., SL 2,, Programming and software engineering.
Responsibility: Ben Liblit.

Abstract:

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. This monograph reconsiders two common assumptions about how we should analyze software and arrives at some striking new results. This new approach makes use of some of the tools that biologists and economists use to understand their complicated systems by considering programs as statistical processes and using statistical techniques to understand software. The centerpiece of the monograph is 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. The results Ben Liblit presents with his thesis represent a new and fundamental approach to software analysis and will provide a source of ideas and inspiration to the field for many years to come.

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