Quality Requirements Analysis with Machine Learning

Tetsuo Tamai, Taichi Anzai

2018

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

Paper Citation

in Harvard Style

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

in Bibtex Style

@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 - Volume 1: ENASE,},
year={2018},
pages={241-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006694502410248},
isbn={978-989-758-300-1},
}

in EndNote Style

TY - CONF

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