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Bayesian network based program dependence graph for fault localization

Version 2 2024-06-06, 09:45
Version 1 2017-03-28, 16:09
conference contribution
posted on 2024-06-06, 09:45 authored by X Yu, J Liu, ZJ Yang, Xiao LiuXiao Liu, X Yin, S Yi
Some probabilistic graphical models such as probabilistic program dependence graph (PPDG) have been used in fault localization. However, PPDG is insufficient to reason across nonadjacent nodes and only support making inference about local anomaly. In this paper, we propose a novel probabilistic graphical model called Bayesian Network based Program Dependence Graph (BNPDG) that has the excellent inference capability for reasoning across nonadjacent nodes. We focus on applying the BNPDG to fault localization. Compared with the PPDG, our BNPDG-based fault localization approach overcomes the reasoning limitation across nonadjacent nodes and provides more precise fault localization by taking its output nodes as the common conditions to calculate the conditional probability of each non-output node. Experiment results show that our BNPDG-based fault localization approach outperforms its rivals.

History

Pagination

181-188

Location

Ottawa, Ont.

Start date

2016-10-23

End date

2016-10-27

ISBN-13

9781509036011

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2016, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

ISSREW 2016 : Proceedings of the 2016 IEEE International Symposium on Software Reliability Engineering Workshops

Event

IEEE Computing Society. Conference (27th : 2016 : Ottawa, Ontario)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Computing Society Conference