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Implicit Social Network Model for Predicting and Tracking the Location of Faults
July 28-August 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/COMPSAC.2008.1622008 32nd Annual IEEE International C ...
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Ing-Xiang Chen, Cheng-Zen Yang, Ting-Kun Lu, Hojun Jaygarl, "Implicit Social Network Model for Predicting and Tracking the Location of Faults," Computer Software and Applications Conference, Annual International, pp. 136-143, 2008 32nd Annual IEEE International Computer Software and Applications Conference, 2008.
 
 
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@article{ 10.1109/COMPSAC.2008.162,
author = {Ing-Xiang Chen and Cheng-Zen Yang and Ting-Kun Lu and Hojun Jaygarl},
title = {Implicit Social Network Model for Predicting and Tracking the Location of Faults},
journal ={Computer Software and Applications Conference, Annual International},
volume = {0},
year = {2008},
issn = {0730-3157},
pages = {136-143},
doi = {http://doi.ieeecomputersociety.org/10.1109/COMPSAC.2008.162},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Computer Software and Applications Conference, Annual International
TI - Implicit Social Network Model for Predicting and Tracking the Location of Faults
SN - 0730-3157
SP136
EP143
A1 - Ing-Xiang Chen,
A1 - Cheng-Zen Yang,
A1 - Ting-Kun Lu,
A1 - Hojun Jaygarl,
PY - 2008
KW - bug report managing system (BRMS)
KW - bug prediction
KW - implicit social network analysis
KW - PageRank
VL - 0
JA - Computer Software and Applications Conference, Annual International
ER -
 
In software testing and maintenance activities, the observed faults and bugs are reported in bug report managing systems (BRMS) for further analysis and repair. According to the information provided by bug reports, developers need to find out the location of these faults and fix them. However, bug locating usually involves intensively browsing back and forth through bug reports and software code and thus incurs unpredictable cost of labor and time. Hence, establishing a robust model to efficiently and effectively locate and track faults is crucial to facilitate software testing and maintenance. In our observation, some related bug locations are tightly associated with the implicit links among source files. In this paper, we present an implicit social network model using PageRank to establish a social network graph with the extracted links. When a new bug report arrives, the prediction model provides users with likely bug locations according to the implicit social network graph constructed from the co-cited source files. The proposed approach has been implemented in real-world software archives and can effectively predict correct bug locations.
Index Terms:
bug report managing system (BRMS), bug prediction, implicit social network analysis, PageRank
Citation:
Ing-Xiang Chen, Cheng-Zen Yang, Ting-Kun Lu, Hojun Jaygarl, "Implicit Social Network Model for Predicting and Tracking the Location of Faults," compsac, pp.136-143, 2008 32nd Annual IEEE International Computer Software and Applications Conference, 2008
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