{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T07:28:19Z","timestamp":1768289299550,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Software requirements are primarily classified into functional and non-functional requirements. While research has explored automated multiclass classification of non-functional requirements, functional requirements remain largely unexplored. This study addressed that gap by introducing a comprehensive dataset comprising 9529 functional requirements from 315 diverse projects. The requirements are classified into five categories: ubiquitous, event-driven, state-driven, unwanted behavior, and optional capabilities. Natural Language Processing (NLP), machine learning (ML), and deep learning (DL) techniques are employed to enable automated classification. All software requirements underwent several procedures, including normalization and feature extraction techniques such as TF-IDF. A series of Machine learning (ML) and deep learning (DL) experiments were conducted to classify subcategories of functional requirements. Among the trained models, the convolutional neural network achieved the highest performance, with an accuracy of 93, followed by the long short-term memory network with an accuracy of 92, outperforming traditional decision-tree-based methods. This work offers a foundation for precise requirement classification tools by providing both the dataset and an automated classification approach.<\/jats:p>","DOI":"10.3390\/systems13070567","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T11:26:37Z","timestamp":1752233197000},"page":"567","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cross-Project Multiclass Classification of EARS-Based Functional Requirements Utilizing Natural Language Processing, Machine Learning, and Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7792-6412","authenticated-orcid":false,"given":"Touseef","family":"Tahir","sequence":"first","affiliation":[{"name":"Department of Computing, University of Roehampton, London SW15 5PJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamid","family":"Jahankhani","sequence":"additional","affiliation":[{"name":"Faculty of Engineering & Environment, Northumbria University, London Campus, London E1 7HT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kinza","family":"Tasleem","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1547-1134","authenticated-orcid":false,"given":"Bilal","family":"Hassan","sequence":"additional","affiliation":[{"name":"Faculty of Engineering & Environment, Northumbria University, London Campus, London E1 7HT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"ref_1","first-page":"55","article-title":"Natural Language Processing for Requirements Engineering","volume":"54","author":"Zhao","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rahimi, N., Eassa, F., and Elrefaei, L. (2020). An Ensemble Machine Learning Technique for Functional Requirement Classification. Symmetry, 12.","DOI":"10.3390\/sym12101601"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pandey, D., Suman, U., and Ramani, A.K. (2010, January 16\u201317). An Effective Requirement Engineering Process Model for Software Development and Requirements Management. Proceedings of the 2010 International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, India.","DOI":"10.1109\/ARTCom.2010.24"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s00766-007-0045-1","article-title":"Automated classification of non-functional requirements","volume":"12","author":"Settimi","year":"2007","journal-title":"Requir. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.infsof.2018.09.004","article-title":"Evaluating different i*-based approaches for selecting functional requirements while balancing and optimizing non-functional requirements: A controlled experiment","volume":"106","author":"Zubcoff","year":"2019","journal-title":"Inf. Softw. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lima, M., Valle, V., Costa, E., Lira, F., and Gadelha, B. (2019, January 23\u201327). Software Engineering Repositories: Expanding the PROMISE Database. Proceedings of the XXXIII Brazilian Symposium on Software Engineering, Salvador, Brazil.","DOI":"10.1145\/3350768.3350776"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mavin, A., Wilkinson, P., Harwood, A., and Novak, M. (October, January 31). Easy Approach to Requirements Syntax (EARS). Proceedings of the 2009 17th IEEE International Requirements Engineering Conference, Atlanta, GA, USA.","DOI":"10.1109\/RE.2009.9"},{"key":"ref_8","unstructured":"Reisig, W. (2012). Petri Nets: An Introduction, Springer Science & Business Media."},{"key":"ref_9","first-page":"1","article-title":"The Formal Specification Language mCRL2","volume":"Volume 6351","author":"Brinksma","year":"2007","journal-title":"Methods for Modelling Software Systems (MMOSS)"},{"key":"ref_10","first-page":"22","article-title":"Identifying Non-functional Requirements from Unconstrained Documents using Natural Language Processing and Machine Learning Approaches","volume":"4","author":"Shreda","year":"2016","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Quba, G.Y., Al Qaisi, H., Althunibat, A., and AlZu\u2019bi, S. (2021, January 14\u201315). Software Requirements Classification using Machine Learning algorithm\u2019s. Proceedings of the 2021 International Conference on Information Technology (ICIT), Amman, Jordan.","DOI":"10.1109\/ICIT52682.2021.9491688"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rahimi, N., Eassa, F., and Elrefaei, L. (2021). One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning. Entropy, 23.","DOI":"10.3390\/e23101264"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7383","DOI":"10.1007\/s00521-019-04226-5","article-title":"Extraction of non-functional requirement using semantic similarity distance","volume":"32","author":"Younas","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Halim, F., and Siahaan, D. (2019, January 22\u201323). Detecting Non-Atomic Requirements in Software Requirements Specifications Using Classification Methods. Proceedings of the 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), Denpasar, Indonesia.","DOI":"10.1109\/ICORIS.2019.8874888"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Navarro-Almanza, R., Juarez-Ramirez, R., and Licea, G. (2017, January 25\u201327). Towards Supporting Software Engineering Using Deep Learning: A Case of Software Requirements Classification. Proceedings of the 2017 5th International Conference in Software Engineering Research and Innovation (CONISOFT), M\u00e9rida, Mexico.","DOI":"10.1109\/CONISOFT.2017.00021"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Slankas, J., and Williams, L. (2013, January 18\u201326). Automated extraction of non-functional requirements in available documentation. Proceedings of the 2013 1st International Workshop on Natural Language Analysis in Software Engineering (NaturaLiSE), San Francisco, CA, USA.","DOI":"10.1109\/NAturaLiSE.2013.6611715"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kurtanovic, Z., and Maalej, W. (2017, January 4\u20138). Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning. Proceedings of the 2017 IEEE 25th International Requirements Engineering Conference (RE), Lisbon, Portugal.","DOI":"10.1109\/RE.2017.82"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Singh, P., Singh, D., and Sharma, A. (2016, January 21\u201324). Rule-based system for automated classification of non-functional requirements from requirement specifications. Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India.","DOI":"10.1109\/ICACCI.2016.7732115"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Taj, S., Arain, Q., Memon, I., and Zubedi, A. (2019, January 9\u201312). To apply Data Mining for Classification of Crowd sourced Software Requirements. Proceedings of the 2019 8th International Conference on Software and Information Engineering, Cairo, Egypt.","DOI":"10.1145\/3328833.3328837"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dalpiaz, F., Dell\u2019Anna, D., Aydemir, F.B., and Cevikol, S. (2019, January 23\u201327). Requirements Classification with Interpretable Machine Learning and Dependency Parsing. Proceedings of the 2019 IEEE 27th International Requirements Engineering Conference (RE), Jeju, Republic of Korea.","DOI":"10.1109\/RE.2019.00025"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Baker, C., Deng, L., Chakraborty, S., and Dehlinger, J. (2019, January 15\u201319). Automatic Multi-class Non-Functional Software Requirements Classification Using Neural Networks. Proceedings of the 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Milwaukee, WI, USA.","DOI":"10.1109\/COMPSAC.2019.10275"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.infsof.2015.07.006","article-title":"A methodology for the classification of quality of requirements using machine learning techniques","volume":"67","author":"Parra","year":"2015","journal-title":"Inf. Softw. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lilleberg, J., Zhu, Y., and Zhang, Y. (2015, January 6\u20138). Support vector machines and Word2vec for text classification with semantic features. Proceedings of the 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Beijing, China.","DOI":"10.1109\/ICCI-CC.2015.7259377"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/THMS.2013.2293535","article-title":"Human\u2013Agent Teaming for Multirobot Control: A Review of Human Factors Issues","volume":"44","author":"Chen","year":"2014","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MS.2025.3549628","article-title":"From Code Generation to Software Testing: AI Copilot with Context-Based Retrieval-Augmented Generation","volume":"42","author":"Wang","year":"2025","journal-title":"IEEE Softw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Dekhtyar, A., and Fong, V. (2017, January 4\u20138). RE Data Challenge: Requirements Identification with Word2Vec and TensorFlow. Proceedings of the 2017 IEEE 25th International Requirements Engineering Conference (RE), Lisbon, Portugal.","DOI":"10.1109\/RE.2017.26"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Canedo, E.D., and Mendes, B.C. (2020). Software Requirements Classification Using Machine Learning Algorithms. Entropy, 22.","DOI":"10.3390\/e22091057"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Raharja, I.M.S., and Siahaan, D.O. (2019, January 18). Classification of Non-Functional Requirements Using Fuzzy Similarity KNN Based on ISO\/IEC 25010. Proceedings of the 2019 12th International Conference on Information & Communication Technology and System (ICTS), Surabaya, Indonesia.","DOI":"10.1109\/ICTS.2019.8850944"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7","DOI":"10.5120\/ijca2017913555","article-title":"Software Requirements Classification using Natural Language Processing and SVD","volume":"164","author":"Mahmoud","year":"2017","journal-title":"IJCA"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Abad, Z.S.H., Karras, O., Ghazi, P., Glinz, M., Ruhe, G., and Schneider, K. (2017, January 4\u20138). What Works Better? A Study of Classifying Requirements. Proceedings of the 2017 IEEE 25th International Requirements Engineering Conference (RE), Lisbon, Portugal.","DOI":"10.1109\/RE.2017.36"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hakim, L., and Rochimah, S. (2018, January 9\u201311). Oversampling Imbalance Data: Case Study on Functional and Non Functional Requirement. Proceedings of the 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), Batu, Indonesia.","DOI":"10.1109\/EECCIS.2018.8692986"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rashwan, A., Ormandjieva, O., and Witte, R. (2013, January 22\u201326). Ontology-Based Classification of Non-functional Requirements in Software Specifications: A New Corpus and SVM-Based Classifier. Proceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference, Kyoto, Japan.","DOI":"10.1109\/COMPSAC.2013.64"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chatterjee, R., Ahmed, A., and Anish, P.R. (2020, January 1). Identification and Classification of Architecturally Significant Functional Requirements. Proceedings of the 2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE), Zurich, Switzerland.","DOI":"10.1109\/AIRE51212.2020.00008"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Winkler, J., and Vogelsang, A. (2016, January 12\u201316). Automatic Classification of Requirements Based on Convolutional Neural Networks. Proceedings of the 2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), Beijing, China.","DOI":"10.1109\/REW.2016.021"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s12065-019-00241-6","article-title":"Automated cloud service based quality requirement classification for software requirement specification","volume":"14","author":"Merugu","year":"2021","journal-title":"Evol. Intel."},{"key":"ref_36","unstructured":"Hirsch, H.-G., and Pearce, D. (2000, January 16\u201320). the aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP 2000), Beijing, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"H\u00e5kansson, E., and Bjarnason, E. (2020, January 31). Including Human Factors and Ergonomics in Requirements Engineering for Digital Work Environments. Proceedings of the 2020 IEEE First International Workshop on Requirements Engineering for Well-Being, Aging, and Health (REWBAH), Zurich, Switzerland.","DOI":"10.1109\/REWBAH51211.2020.00013"},{"key":"ref_38","unstructured":"(2018). International Organization for StandardizationSystems and Software Engineering\u2014Life Cycle Processes\u2014Requirements Engineering. (Standard No. ISO\/IEC\/IEEE 29148:2018). International Electrotechnical Commission."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1109\/TSE.2021.3051898","article-title":"The Effects of Human Aspects on the Requirements Engineering Process: A Systematic Literature Review","volume":"48","author":"Hidellaarachchi","year":"2022","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_40","unstructured":"Christiano, P.F., Leike, J., Brown, T.B., Martic, M., Legg, S., and Amodei, D. (2017, January 4\u20139). Deep Reinforcement Learning from Human Preferences. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA. in NIPS\u201917."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wong, M.F., and Tan, C.W. (2024). Aligning Crowd-Sourced Human Feedback for Reinforcement Learning on Code Generation by Large Language Models. IEEE Trans. Big Data, 1\u201312.","DOI":"10.1109\/TBDATA.2024.3524104"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/7\/567\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:08:02Z","timestamp":1760033282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/7\/567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,10]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["systems13070567"],"URL":"https:\/\/doi.org\/10.3390\/systems13070567","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,10]]}}}