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Supporting viewpoints to review the lack of requirements in space systems with machine learning

Published: 18 September 2020 Publication History

Abstract

Identifying the insufficient requirements, such as missing or lacking requirements, is important to prevent serious accidents for CPSs (Cyber-Physical Systems), such as launch vehicles and spacecrafts which often required to be self-adaptive. In JAXA (Japan Aerospace Exploration Agency), several review boards in place to verify the requirements toward space systems from various viewpoints which often derived from the reviewer's experience about anomalies. However, the impossibility of assigning a well-experienced reviewer to all review opportunities highlights the importance of sharing their viewpoints. In this paper, we aimed to extract and exploit associations between development documents and archived anomaly reports about space systems, thereby identifying the lacking requirement in the review. An association between these two documents can be treated as a viewpoint of the review, which contributes to preventing previously experienced anomalies. To cope with this problem, we propose a CNN (Convolutional Neural Network) model that predicts the correlation of documents. A collection of judgments about meaningfully related pairs of text was prepared to train the proposed model to contribute to detect the lack of requirements. Experimental results have shown that the performance of the proposed method is significantly better than that of baseline methods (i.e., 71.0% in F-measure). Also, further investigation has shown that not only the word similarity, but different attributes are necessary to solve our problem.

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Cited By

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  • (2023)Verification & Validation Methods for Complex AI-enabled Cyber-Physical Learning-Based Systems: A Systematic Literature Review2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)10.1109/ICE/ITMC58018.2023.10332308(1-7)Online publication date: 19-Jun-2023
  • (2022)Reducing large adaptation spaces in self-adaptive systems using classical machine learningJournal of Systems and Software10.1016/j.jss.2022.111341190:COnline publication date: 1-Aug-2022
  • (2021)If a System is Learning to Self-adapt, Who's Teaching?2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)10.1109/SEAMS51251.2021.00043(256-257)Online publication date: May-2021

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cover image ACM Conferences
SEAMS '20: Proceedings of the IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
June 2020
211 pages
ISBN:9781450379625
DOI:10.1145/3387939
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 September 2020

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Author Tags

  1. machine learning
  2. neural networks
  3. software requirement
  4. verification
  5. viewpoint

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SEAMS '20
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Overall Acceptance Rate 17 of 31 submissions, 55%

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View all
  • (2023)Verification & Validation Methods for Complex AI-enabled Cyber-Physical Learning-Based Systems: A Systematic Literature Review2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)10.1109/ICE/ITMC58018.2023.10332308(1-7)Online publication date: 19-Jun-2023
  • (2022)Reducing large adaptation spaces in self-adaptive systems using classical machine learningJournal of Systems and Software10.1016/j.jss.2022.111341190:COnline publication date: 1-Aug-2022
  • (2021)If a System is Learning to Self-adapt, Who's Teaching?2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)10.1109/SEAMS51251.2021.00043(256-257)Online publication date: May-2021

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