research-article

A novel approach to tracing safety requirements and state-based design models

Authors:

Mounifah Alenazi,

Juha SavolainenAuthors Info & Claims

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

Pages 848 - 860

https://doi.org/10.1145/3377811.3380332

Published: 01 October 2020 Publication History

Abstract

Traceability plays an essential role in assuring that software and systems are safe to use. Automated requirements traceability faces the low precision challenge due to a large number of false positives being returned and mingled with the true links. To overcome this challenge, we present a mutation-driven method built on the novel idea of proactively creating many seemingly correct tracing targets (i.e., mutants of a state machine diagram), and then exploiting model checking within process mining to automatically verify whether the safety requirement's properties hold in the mutants. A mutant is killed if its model checking fails; otherwise, it is survived. We leverage the underlying killed-survived distinction, and develop a correlation analysis procedure to identify the traceability links. Experimental evaluation results on two automotive systems with 27 safety requirements show considerable precision improvements compared with the state-of-the-art.

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Cited By

Luitel DNejati SSabetzadeh M(2024)Requirements-Driven Slicing of Simulink Models using LLMs2024 IEEE 32nd International Requirements Engineering Conference Workshops (REW)10.1109/REW61692.2024.00014(72-82)Online publication date: 24-Jun-2024
https://doi.org/10.1109/REW61692.2024.00014
Sultan BFrénot LApvrille LJaillon PCoudert S(2023)AMULET: a Mutation Language Enabling Automatic Enrichment of SysML ModelsACM Transactions on Embedded Computing Systems10.1145/3624583Online publication date: 16-Sep-2023
https://dl.acm.org/doi/10.1145/3624583
Li MYang LXia QFang MLiang GZuo C(2022)STPChain: a Crowdsourced Software Engineering Method for Software Traceability and Fine-grained Privacy Based on Blockchain2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00135(849-859)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00135
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Index Terms

A novel approach to tracing safety requirements and state-based design models
1. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Requirements analysis
    2. Software verification and validation
      1. Process validation
        Traceability
  2. Software notations and tools
    1. System description languages
      1. System modeling languages

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cover image ACM Conferences

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

June 2020

1640 pages

ISBN:9781450371216

DOI:10.1145/3377811

General Chairs:
Gregg Rothermel
North Carolina State University
,
Doo-Hwan Bae
KAIST, South Korea

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Cited By

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https://doi.org/10.1109/REW61692.2024.00014
Sultan BFrénot LApvrille LJaillon PCoudert S(2023)AMULET: a Mutation Language Enabling Automatic Enrichment of SysML ModelsACM Transactions on Embedded Computing Systems10.1145/3624583Online publication date: 16-Sep-2023
https://dl.acm.org/doi/10.1145/3624583
Li MYang LXia QFang MLiang GZuo C(2022)STPChain: a Crowdsourced Software Engineering Method for Software Traceability and Fine-grained Privacy Based on Blockchain2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC54236.2022.00135(849-859)Online publication date: Jun-2022
https://doi.org/10.1109/COMPSAC54236.2022.00135
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Jin ZNiu NYu Y(2021)An RE’21 Workshop on Environment-Driven Requirements Engineering (EnviRE’21)2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)10.1109/REW53955.2021.00075(421-421)Online publication date: Sep-2021
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Paucar LBencomo NSutcliffe A(2021)Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)10.1109/REW53955.2021.00026(146-152)Online publication date: Sep-2021
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Gudaparthi HNiu NWang BSavolainen J(2021)Reliability of Convolutional Neural Networks: Failure Metrics with Metamorphic Test Cases2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI51335.2021.00017(75-82)Online publication date: 10-Aug-2021
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