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Discovering Neglected Conditions in Software by Mining Dependence Graphs
Sept.-Oct. 2008 (vol. 34 no. 5)
pp. 579-596
ASCII Text
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Ray-Yaung Chang, Andy Podgurski, Jiong Yang, "Discovering Neglected Conditions in Software by Mining Dependence Graphs," IEEE Transactions on Software Engineering, vol. 34, no. 5, pp. 579-596, Sept.-Oct., 2008.
 
 
BibTex
x
 
@article{ 10.1109/TSE.2008.24,
author = {Ray-Yaung Chang and Andy Podgurski and Jiong Yang},
title = {Discovering Neglected Conditions in Software by Mining Dependence Graphs},
journal ={IEEE Transactions on Software Engineering},
volume = {34},
number = {5},
issn = {0098-5589},
year = {2008},
pages = {579-596},
doi = {http://doi.ieeecomputersociety.org/10.1109/TSE.2008.24},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Software Engineering
TI - Discovering Neglected Conditions in Software by Mining Dependence Graphs
IS - 5
SN - 0098-5589
SP579
EP596
EPD - 579-596
A1 - Ray-Yaung Chang,
A1 - Andy Podgurski,
A1 - Jiong Yang,
PY - 2008
KW - Methods for SQA and V&V
KW - Pre- and post-conditions
VL - 34
JA - IEEE Transactions on Software Engineering
ER -
 
Ray-Yaung Chang, Case Western Reserve University, Cleveland
Andy Podgurski, Case Western Reserve University, Cleveland
Jiong Yang, Case Western Reserve University, Cleveland
Neglected conditions are an important but difficult-to-find class of software defects. This paper presents a novel approach to revealing neglected conditions that integrates static program analysis and advanced data mining techniques to discover implicit conditional rules in a code base and to discover rule violations that indicate neglected conditions. The approach requires the user to indicate minimal constraints on the context of the rules to be sought, rather than specific rule templates. To permit this generality, rules are modeled as graph minors of enhanced procedure dependence graphs (EPDGs), in which control and data dependence edges are augmented by edges representing shared data dependences. A heuristic maximal frequent subgraph mining algorithm is used to extract candidate rules from EPDGs, and a heuristic graph matching algorithm is used to identify rule violations. We also report the results of an empirical study in which the approach was applied to four open source projects (openssl, make, procmail, amaya). These results indicate that the approach is effective and reasonably efficient.

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Index Terms:
Methods for SQA and V&V, Pre- and post-conditions
Citation:
Ray-Yaung Chang, Andy Podgurski, Jiong Yang, "Discovering Neglected Conditions in Software by Mining Dependence Graphs," IEEE Transactions on Software Engineering, vol. 34, no. 5, pp. 579-596, Sept.-Oct. 2008, doi:10.1109/TSE.2008.24
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