research-article

Software Engineering Repositories: Expanding the PROMISE Database

Authors:

Estevão Costa,

Bruno GadelhaAuthors Info & Claims

SBES '19: Proceedings of the XXXIII Brazilian Symposium on Software Engineering

Pages 427 - 436

https://doi.org/10.1145/3350768.3350776

Published: 23 September 2019 Publication History

Abstract

Defining and classifying software requirements are critical tasks for determining software functionality and overall software architecture. In this sense, several types of research are being developed aiming to automate the classification of software requirements through the use of machine learning algorithms. However, the feasibility of such studies runs counter to the existence of a public database that is adequate in terms of quantity and quality of sample requirements. A requirement base widely used in this type of task is the PROMISE. However, the number of requirements is considered low for practical applications involving machine learning. This research presents an expansion of the PROMISE corpus. New software requirements were incorporated, and the resulting dataset was evaluated through the use of well-known machine learning algorithms. We observed some improvement in the performance of these algorithms regarding the identification of some types of software requirements.

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Cited By

Subahi A(2025)Enhancing Software Sustainability: Leveraging Large Language Models to Evaluate Security Requirements Fulfillment in Requirements EngineeringSystems10.3390/systems1302011413:2(114)Online publication date: 12-Feb-2025
https://doi.org/10.3390/systems13020114
Zhao LAlhoshan W(2025)Machine Learning for Requirements ClassificationHandbook on Natural Language Processing for Requirements Engineering10.1007/978-3-031-73143-3_2(19-59)Online publication date: 6-Mar-2025
https://doi.org/10.1007/978-3-031-73143-3_2
Althunibat AAlsawareah BMaidin SHawashin BJebril IZaqaibeh BAl-khawaja H(2024)Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning AlgorithmsInternational Journal of Cloud Applications and Computing10.4018/IJCAC.33956314:1(1-19)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.4018/IJCAC.339563
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Index Terms

Software Engineering Repositories: Expanding the PROMISE Database
1. Software and its engineering
  1. Software notations and tools
    1. Software libraries and repositories

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cover image ACM Other conferences

SBES '19: Proceedings of the XXXIII Brazilian Symposium on Software Engineering

September 2019

583 pages

ISBN:9781450376518

DOI:10.1145/3350768

General Chairs:
Ivan Machado
UFBA
,
Rodrigo Souza
UFBA
,
Rita Suzana Maciel
UFBA
,
Claudio Sant'Anna
UFBA

Copyright © 2019 ACM.

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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SBC: Sociedade Brasileira de Computação

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2019

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Research-article
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Funding Sources

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Fundação de Amparo à Pesquisa do Estado do Amazonas

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SBES 2019

SBES 2019: XXXIII Brazilian Symposium on Software Engineering

September 23 - 27, 2019

Salvador, Brazil

Acceptance Rates

SBES '19 Paper Acceptance Rate 67 of 153 submissions, 44%;

Overall Acceptance Rate 147 of 427 submissions, 34%

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Cited By

Subahi A(2025)Enhancing Software Sustainability: Leveraging Large Language Models to Evaluate Security Requirements Fulfillment in Requirements EngineeringSystems10.3390/systems1302011413:2(114)Online publication date: 12-Feb-2025
https://doi.org/10.3390/systems13020114
Zhao LAlhoshan W(2025)Machine Learning for Requirements ClassificationHandbook on Natural Language Processing for Requirements Engineering10.1007/978-3-031-73143-3_2(19-59)Online publication date: 6-Mar-2025
https://doi.org/10.1007/978-3-031-73143-3_2
Althunibat AAlsawareah BMaidin SHawashin BJebril IZaqaibeh BAl-khawaja H(2024)Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning AlgorithmsInternational Journal of Cloud Applications and Computing10.4018/IJCAC.33956314:1(1-19)Online publication date: 9-Apr-2024
https://dl.acm.org/doi/10.4018/IJCAC.339563
Hassan SLi QZubair MAlsowail RQureshi M(2024)Unveiling the Correlation between Nonfunctional Requirements and Sustainable Environmental Factors Using a Machine Learning ModelSustainability10.3390/su1614590116:14(5901)Online publication date: 11-Jul-2024
https://doi.org/10.3390/su16145901
Airlangga G(2024)Enhancing Software Requirements Classification with Semisupervised GAN‐BERT TechniqueJournal of Electrical and Computer Engineering10.1155/2024/49556912024:1Online publication date: 30-Jul-2024
https://doi.org/10.1155/2024/4955691
Sampaio SLima Mde Souza EMeireles MPessoa MConte T(2024)Exploring the Use of Large Language Models in Requirements Engineering Education: An Experience Report with ChatGPT 3.5Proceedings of the XXIII Brazilian Symposium on Software Quality10.1145/3701625.3701687(624-634)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3701625.3701687
Siddeshwar VAlwidian SMakrehchi M(2024)A Systematic Review of AI-Enabled Frameworks in Requirements ElicitationIEEE Access10.1109/ACCESS.2024.347529312(154310-154336)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3475293
Abbas JZhang CLuo B(2024)EnsCL-CatBoost: A Strategic Framework for Software Requirements ClassificationIEEE Access10.1109/ACCESS.2024.345201112(127614-127628)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3452011
Rahman KGhani AMisra SRahman A(2024)A deep learning framework for non-functional requirement classificationScientific Reports10.1038/s41598-024-52802-014:1Online publication date: 8-Feb-2024
https://doi.org/10.1038/s41598-024-52802-0
Das SDeb NCortesi AChaki N(2024)Extracting goal models from natural language requirement specificationsJournal of Systems and Software10.1016/j.jss.2024.111981211(111981)Online publication date: May-2024
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