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Software reliability via machine learning (invited talk)

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Aditya V. NoriAuthors Info & Claims

FormaliSE 2014: Proceedings of the 2nd FME Workshop on Formal Methods in Software Engineering

Pages 1 - 2

https://doi.org/10.1145/2593489.2593490

Published: 03 June 2014 Publication History

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Abstract

Computing good invariants is key to effective and efficient program verification. In this talk I will describe our experiences in using machine learning techniques (support vector machines, linear regression) for computing invariants useful for program verification as well as for fine tuning verification tools.

References

[1]

A. V. Nori and R. Sharma. Termination proofs from tests. In Foundations of Software Engineering (FSE), pages 246–256, 2013.

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[2]

R. Sharma, A. V. Nori, and A. Aiken. Interpolants as classifiers. In Computer Aided Verification (CAV), pages 71–87, 2012.

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[3]

R. Sharma, A. V. Nori, and A. Aiken. Bias-variance tradeoffs in program analysis. In Principles of Programming Languages (POPL), pages 127–138, 2014.

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Software reliability via machine learning (invited talk)

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Published In

FormaliSE 2014: Proceedings of the 2nd FME Workshop on Formal Methods in Software Engineering

June 2014

58 pages

ISBN:9781450328531

DOI:10.1145/2593489

Program Chairs:
Stefania Gnesi,
Nico Plat

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TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2014

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ICSE '14

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SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

June 3, 2014

Hyderabad, India

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ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

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