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Author, Editor

Author(s):

Dietz, Laura
Dallmeier, Valentin

dblp
dblp

Not MPG Author(s):

Dallmeier, Valentin

Editor(s):





BibTeX cite key*:

DietzDallmeier2008

Title, Booktitle

Title*:

Probabilistic Graph Models for Debugging Software


dietz-graph-models-for-debugging-v1.1.pdf (654.54 KB)

Booktitle*:

Proceedings of NIPS 2008 Workshop on Analyzing Graphs: Theory and Applications

Event, URLs

URL of the conference:

http://research.yahoo.com/workshops/nipsgraphs2008/index.html

URL for downloading the paper:


Event Address*:

Whistler, Canada

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

12 December 2008

Event End Date:

12 December 2008

Publisher

Name*:


This proceedings has no publisher!

URL:


Address*:

s.l.

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:

December

Pages:

1-8

Year*:

2008

VG Wort Pages:


ISBN/ISSN:


Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

Of all software development activities, debugging---locating the defective source code statements that cause a failure---can be by far the most time-consuming. We employ probabilistic modeling to support programmers in finding defective code. Most defects are identifiable in control flow graphs of software traces. A trace is represented by a sequence of code positions (line numbers in source filenames) that are executed when the software runs. The control flow graph represents the finite state machine of the program, in which states depict code positions and arcs indicate valid follow up code positions. In this work, we extend this definition towards an n-gram control flow graph, where a state represents a fragment of subsequent code positions, also referred to as an n-gram of code positions. We devise a probabilistic model for such graphs in order to infer code positions in which anomalous program behavior can be observed. This model is evaluated on real world data obtained from the open source AspectJ project and compared to the well known multinomial and multi-variate Bernoulli model.



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Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Machine Learning

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MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{DietzDallmeier2008,
AUTHOR = {Dietz, Laura and Dallmeier, Valentin},
TITLE = {Probabilistic Graph Models for Debugging Software},
BOOKTITLE = {Proceedings of NIPS 2008 Workshop on Analyzing Graphs: Theory and Applications},
YEAR = {2008},
PAGES = {1--8},
ADDRESS = {Whistler, Canada},
MONTH = {December},
}


Entry last modified by Anja Becker, 03/23/2010
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Editor(s)
Laura Dietz
Created
02/08/2010 05:22:20 PM
Revisions
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Editor(s)
Anja Becker
Laura Dietz
Laura Dietz
Laura Dietz
Edit Dates
23.03.2010 10:24:50
30.01.2009 12:23:21
30.01.2009 12:11:43
30.01.2009 12:03:14
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