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Authors: Tetsuo Tamai 1 and Taichi Anzai 2

Affiliations: 1 Hosei University, Japan ; 2 NTT Com Solutions Corp., Japan

Keyword(s): Quality Requirements, Machine Learning, Requirements Classification, Natural Language Processing.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Requirements Engineering ; Symbolic Systems

Abstract: The importance of software quality requirements (QR) is being widely recognized, which motivates studies that investigate software requirements specifications (SRS) in practice and collect data on how much QR are written vs.\ functional requirements (FR) and what kind of QR are specified. It is useful to develop a tool that automates the process of filtering out QR statements from an SRS and classifying them into the quality characteristic attributes such as defined in the ISO/IEC 25000 quality model. We propose an approach that uses a machine learning technique to mechanize the process. With this mechanism, we can identify how each QR characteristic scatters over the document, i.e. how much in volume and in what way. A tool \textit{QRMiner} is developed to support the process and case studies were conducted, taking thirteen SRS documents that were written for real use. We report our findings from these cases

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Paper citation in several formats:
Tamai, T., Anzai and T. (2018). Quality Requirements Analysis with Machine Learning. In Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-300-1; ISSN 2184-4895, SciTePress, pages 241-248. DOI: 10.5220/0006694502410248

@conference{enase18,
author={Tetsuo Tamai and Taichi Anzai},
title={Quality Requirements Analysis with Machine Learning},
booktitle={Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2018},
pages={241-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006694502410248},
isbn={978-989-758-300-1},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Quality Requirements Analysis with Machine Learning
SN - 978-989-758-300-1
IS - 2184-4895
AU - Tamai, T.
AU - Anzai, T.
PY - 2018
SP - 241
EP - 248
DO - 10.5220/0006694502410248
PB - SciTePress

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