Paper Info
Title
Dual analysis for proving safety and finding bugs
Abstract
Program bugs remain a major challenge for software developers and various tools have been proposed to help with their localisation and elimination. Most present-day tools are based either on over-approximating techniques that can prove safety but may report false positives, or on under-approximating techniques that can find real bugs but with possible false negatives. In this paper, we propose a dual static analysis that is based only on over-approximation. Its main novelty is to concurrently derive conditions that lead to either success or failure outcomes and thus we provide a comprehensive solution for both proving safety and finding real program bugs. We have proven the soundness of our approach and have implemented a prototype system that is validated by a set of experiments.
Year
DOI
Venue
2013
10.1016/j.scico.2012.07.004
ACM Symposium on Applied Computing
Keywords
Field
DocType
failure outcome,dual static analysis,dual analysis,false positive,main novelty,program bug,real bug,derive condition,comprehensive solution,real program bug,possible false negative,static analysis
Data mining,Programming language,Computer science,Static analysis,Software,Artificial intelligence,Novelty,Soundness,Machine learning,False positive paradox
Journal
Volume
Issue
ISSN
78
4
0167-6423
Citations 
PageRank 
References 
7
0.45
33
Authors
2
Name
Order
Citations
PageRank
Corneliu Popeea137418.27
Wei-Ngan Chin286863.37