Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.scico.2012.07.004
Title: Dual analysis for proving safety and finding bugs
Authors: Popeea, C.
Chin, W.-N. 
Keywords: Automated verification
False positive
Numerical abstract domain
Static analysis
Issue Date: 2013
Source: Popeea, C., Chin, W.-N. (2013). Dual analysis for proving safety and finding bugs. Science of Computer Programming 78 (4) : 390-411. ScholarBank@NUS Repository. https://doi.org/10.1016/j.scico.2012.07.004
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. © 2012 Elsevier B.V. All rights reserved.
Source Title: Science of Computer Programming
URI: http://scholarbank.nus.edu.sg/handle/10635/42058
ISSN: 01676423
DOI: 10.1016/j.scico.2012.07.004
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