article

Mining specifications

Authors:

Rastislav Bodík,

James R. LarusAuthors Info & Claims

ACM SIGPLAN Notices, Volume 37, Issue 1

Pages 4 - 16

https://doi.org/10.1145/565816.503275

Published: 01 January 2002 Publication History

Abstract

Program verification is a promising approach to improving program quality, because it can search all possible program executions for specific errors. However, the need to formally describe correct behavior or errors is a major barrier to the widespread adoption of program verification, since programmers historically have been reluctant to write formal specifications. Automating the process of formulating specifications would remove a barrier to program verification and enhance its practicality.This paper describes specification mining, a machine learning approach to discovering formal specifications of the protocols that code must obey when interacting with an application program interface or abstract data type. Starting from the assumption that a working program is well enough debugged to reveal strong hints of correct protocols, our tool infers a specification by observing program execution and concisely summarizing the frequent interaction patterns as state machines that capture both temporal and data dependences. These state machines can be examined by a programmer, to refine the specification and identify errors, and can be utilized by automatic verification tools, to find bugs.Our preliminary experience with the mining tool has been promising. We were able to learn specifications that not only captured the correct protocol, but also discovered serious bugs.

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Cited By

Germiniani SNicoletti DPravadelli G(2024)Invited Talk: Pros and Cons of Assertion Mining2024 IEEE 25th Latin American Test Symposium (LATS)10.1109/LATS62223.2024.10534611(1-2)Online publication date: 9-Apr-2024
https://doi.org/10.1109/LATS62223.2024.10534611
Rostami HHosseini MAzarpeyvand AIman MGhasempouri T(2024)Automatic High Functional Coverage Stimuli Generation for Assertion-based Verification2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS)10.1109/IOLTS60994.2024.10616069(1-7)Online publication date: 3-Jul-2024
https://doi.org/10.1109/IOLTS60994.2024.10616069
Bozzano MCimatti ACristoforetti MGriggio ASvaizer PTonetta S(2024)Towards Formal Design of FDIR Components with AILeveraging Applications of Formal Methods, Verification and Validation. Software Engineering Methodologies10.1007/978-3-031-75387-9_19(311-328)Online publication date: 26-Oct-2024
https://doi.org/10.1007/978-3-031-75387-9_19
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Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 37, Issue 1

Jan. 2002

342 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/565816

Issue’s Table of Contents

POPL '02: Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
January 2002
351 pages
ISBN:1581134509
DOI:10.1145/503272
Conference Chair:
John Launchbury
OGI and Galois Connections
,
Program Chair:
John C. Mitchell
Stanford University

Copyright © 2002 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 January 2002

Published in SIGPLAN Volume 37, Issue 1

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Cited By

Germiniani SNicoletti DPravadelli G(2024)Invited Talk: Pros and Cons of Assertion Mining2024 IEEE 25th Latin American Test Symposium (LATS)10.1109/LATS62223.2024.10534611(1-2)Online publication date: 9-Apr-2024
https://doi.org/10.1109/LATS62223.2024.10534611
Rostami HHosseini MAzarpeyvand AIman MGhasempouri T(2024)Automatic High Functional Coverage Stimuli Generation for Assertion-based Verification2024 IEEE 30th International Symposium on On-Line Testing and Robust System Design (IOLTS)10.1109/IOLTS60994.2024.10616069(1-7)Online publication date: 3-Jul-2024
https://doi.org/10.1109/IOLTS60994.2024.10616069
Bozzano MCimatti ACristoforetti MGriggio ASvaizer PTonetta S(2024)Towards Formal Design of FDIR Components with AILeveraging Applications of Formal Methods, Verification and Validation. Software Engineering Methodologies10.1007/978-3-031-75387-9_19(311-328)Online publication date: 26-Oct-2024
https://doi.org/10.1007/978-3-031-75387-9_19
Wen CCao JSu JXu ZQin SHe MLi HCheung STian C(2024)Enchanting Program Specification Synthesis by Large Language Models Using Static Analysis and Program VerificationComputer Aided Verification10.1007/978-3-031-65630-9_16(302-328)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-3-031-65630-9_16
Shou XBhattacharjya DGao TSubramanian DHassanzadeh OBennett KKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Probabilistic attention-to-influence neural models for event sequencesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619720(31657-31674)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619720
Mora FDesai APolgreen ESeshia S(2023)Message Chains for Distributed System VerificationProceedings of the ACM on Programming Languages10.1145/36228767:OOPSLA2(2224-2250)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622876
Yan AZhong HSong DJia L(2023)How do programmers fix bugs as workarounds? An empirical study on Apache projectsEmpirical Software Engineering10.1007/s10664-023-10318-728:4Online publication date: 28-Jun-2023
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Wu YXu ZQin S(2023)Detecting API-Misuse Based on Pattern Mining via API Usage Graph with ParametersTheoretical Aspects of Software Engineering10.1007/978-3-031-35257-7_21(344-363)Online publication date: 27-Jun-2023
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Meddah IGuerroudji FRemil N(2022)Distributed Business Process Discovery in Cloud ClustersInternational Journal of Distributed Artificial Intelligence10.4018/IJDAI.30121314:1(1-18)Online publication date: 1-Jan-2022
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Ataiefard FMashhadi MHemmati HWalkinshaw N(2022)Deep State Inference: Toward Behavioral Model Inference of Black-Box Software SystemsIEEE Transactions on Software Engineering10.1109/TSE.2021.312882048:12(4857-4872)Online publication date: 1-Dec-2022
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