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Inference and test generation using program invariants in chemical reaction networks

Authors:

Michael C. Gerten,

Alexis L. Marsh,

James I. Lathrop,

Andrew S. Miner,

Titus H. KlingeAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

Pages 1193 - 1205

https://doi.org/10.1145/3510003.3510176

Published: 05 July 2022 Publication History

Abstract

Chemical reaction networks (CRNs) are an emerging distributed computational paradigm where programs are encoded as a set of abstract chemical reactions. CRNs can be compiled into DNA strands which perform the computations in vitro, creating a foundation for intelligent nanodevices. Recent research proposed a software testing framework for stochastic CRN programs in simulation, however, it relies on existing program specifications. In practice, specifications are often lacking and when they do exist, transforming them into test cases is time-intensive and can be error prone. In this work, we propose an inference technique called ChemFlow which extracts 3 types of invariants from an existing CRN model. The extracted invariants can then be used for test generation or model validation against program implementations. We applied ChemFlow to 13 CRN programs ranging from toy examples to real biological models with hundreds of reactions. We find that the invariants provide strong fault detection and often exhibit less flakiness than specification derived tests. In the biological models we showed invariants to developers and they confirmed that some of these point to parts of the model that are biologically incorrect or incomplete suggesting we may be able to use ChemFlow to improve model quality.

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Cited By

Cohen Md'Amorim M(2024)It’s Organic: Software Testing of Emerging Domains (Keynote)Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3674720(2-3)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3674720
Gerten MLathrop JCohen MChristakis MPradel M(2024)Traceback: A Fault Localization Technique for Molecular ProgramsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652138(415-427)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3652138
Gerten M(2022)A Framework for Testing Chemical Reaction NetworksProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3559562(1-5)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3559562

Index Terms

Inference and test generation using program invariants in chemical reaction networks
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Published In

cover image ACM Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
,
Andreas Zeller
CISPA, Germany

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution-NoDerivatives International 4.0 License.

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Published: 05 July 2022

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May 21 - 29, 2022

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Cited By

Cohen Md'Amorim M(2024)It’s Organic: Software Testing of Emerging Domains (Keynote)Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3674720(2-3)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3663529.3674720
Gerten MLathrop JCohen MChristakis MPradel M(2024)Traceback: A Fault Localization Technique for Molecular ProgramsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3652138(415-427)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3652138
Gerten M(2022)A Framework for Testing Chemical Reaction NetworksProceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering10.1145/3551349.3559562(1-5)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3551349.3559562

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