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Residual dynamic typestate analysis exploiting static analysis: results to reformulate and reduce the cost of dynamic analysis
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Automated Software Engineering archive
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering table of contents
Atlanta, Georgia, USA
SESSION: Program analysis table of contents
Pages 124-133  
Year of Publication: 2007
ISBN:978-1-59593-882-4
Authors
Matthew B. Dwyer  University of Nebraska, Lincoln, NE
Rahul Purandare  University of Nebraska, Lincoln, NE
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGSOFT: ACM Special Interest Group on Software Engineering
Publisher
ACM  New York, NY, USA
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ABSTRACT

Programmers using complex libraries and frameworks are faced with the difficult task of ensuring that their implementations comply with complex and informally described rules for proper sequencing of API calls. Recent advances in static and dynamic techniques for checking explicit specifications of program typestate properties have shown promise in addressing this challenge. Unfortunately, static typestate analyses are limited in their scalability and dynamic analyses can suffer from significant run-time overhead. In this paper, we present an approach that exploits information calculated by flow-sensitive static typestate analyses to reformulate the original analysis problem as a residual dynamic typestate analysis. We demonstrate that residual analyses retain the error reporting of unoptimized dynamic analysis while offering the potential for significantly reducing analysis cost


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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E. Bodden, L. Hendren, and O. Lhotak. A staged static program analysis to improve the performance of runtime monitoring. In 21st Euro. Conf. on Obj.-Oriented Prog., 2007.
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R. Vallée-Rai. SOOT: A Java bytecode optimization framework. Master's thesis, School of Computer Science, McGill University, Montreal, Canada., Oct 2000.


Collaborative Colleagues:
Matthew B. Dwyer: colleagues
Rahul Purandare: colleagues