K. I. Gaydamaka Applying Machine Learning Techniques for Requirements Quality Control
K. I. Gaydamaka Applying Machine Learning Techniques for Requirements Quality Control

The quality of requirements is critical to the success of complex technical systems projects. The paper presents the main procedures for quality control of requirements and the main directions of instrumental support for quality control of requirements. The shortcomings of the existing tools of instrumental support are listed. To overcome these shortcomings, it is proposed to apply machine learning algorithms. The main directions of research in the field of application of machine learning algorithms in the problems of requirements quality control are proposed. The experimental results obtained by the author, demonstrating the feasibility of the proposed approach, are presented. In some tasks, it was possible to achieve a quality of assessment comparable to that of an expert.


requirements engineering; requirements quality; machine learning.

PP. 87-96.

DOI 10.14357/20718632230109

1. V.K. Batovrin, K.I. Gaydamaka, Requirements engineering is a key success factor for projects // Project and program management. 2017. No. 01(49). pp. 6–20.
2. SAE Standard ARP 4754A Guidelines for the Development of Civil Aircraft and Systems. - Revised: 2010-12-21. URL:
3. Radio Technical Commission for Aeronautics RTCA/DO-178B "Software Considerations in Airborne Systems and Equipment Certification". - 1992.
4. ISO 26262-9:2011(en), Road vehicles — Functional safety
5. EN 50128 Railway applications - Communication, signaling and processing systems - Software for railway control and protection systems
6. Good G. Kh., Makol R. E. System engineering. Introduction to large systems design. M.: Soviet radio, 1962. 384 p.
7. A. D. Hall, Experience in Methodology for System Engineering. M.: Soviet radio, 1975. 448 p.
8. Hooks I. Writing Good Requirements // Proceeding's of the Third International Symposium of the INCOSE. 1993. Volume 2.
9. IEEE/ISO/IEC 29148-2018 - ISO/IEC/IEEE International Standard - Systems and software engineering - Life cycle processes - Requirements engineering, Dec. 2020.
10. INCOSE, “Guide for writing requirements INCOSE TP-2010-006-02,” 73, 2015.
11. Pohl K. Requirements Engineering: Fundamentals, Principles, and Techniques. – Springer-Verlag. – 2010.
12. John C. Knight and E. Ann Myers. 1991. Phased inspections and their implementation. SIGSOFT Software. Eng. Notes 16, 3 (July 1991), 29–35.
13. Sharma, Meena & Vishwakarma, Rajeev. (2012). Evaluation & Validation Of Work Products In Unified Software Development Process. International Journal of Software Engineering & Applications. 3. 10.5121/ijsea.2012.3208.
14. Genova G., Fuentes J., Llorens J. et al. A framework to measure and improve the quality of textual requirements // Requir. Eng. 2013. No. 18:25–41. pp. 25–41
15. Femmer H - “Requirements quality defect detection with the qualicen requirements scout” CEUR Workshop Proceedings 2018 vol: 2075
16. Post, Amalinda and Thomas Fuhr. “Case study: How Well Can IBM's "Requirements Quality Assistant" Review Automotive Requirements?” REFSQ Workshops (2021).
17. Gallego, Elena & Chalé, Hugo & Llorens, Juan & Fuentes, José M. & Alvarez-Rodríguez, Jose & Genova, Gonzalo & Fraga, Anabel. (2017). Requirements Quality Analysis: A Successful Case Study in the Industry. 10.1007/978-3-319-49103-5_14.
18. Batrovrin V., Gaydamaka K. Automated System for Requirements Assessment // Proceedings - 2019 Actual Problems of Systems and Software Engineering, APSSE 2019, 2019, pp. 58–62.
19. Mitchell T. Machine Learning. — McGraw-Hill Science/Engineering/Math, 1997.
20. Belonogova A.D., Ognyanovich P.A., Gaidamaka K.I. Application of machine learning methods to ensure the quality of requirements specifications // International Journal of Open Information Technologies. 2021.vol. 9, no. eight.
21. Gaidamaka K.I. A method for assessing the quality of technical requirements based on part-of-talk templates and a metric classifier // Informatization and communication. 2021. No. 8.
22. Ramos J. Using TF-IDF to determine word relevance in document queries // Proceedings of the first instructional conference on machine learning. 2003, 4 p.
23. Rong X. Word2vec parameter learning explained // arXiv preprint arXiv:1411.2738. 2014 Nov 11.
24. Lau J. H., Baldwin T. An empirical evaluation of doc2vec with practical insights into document embedding generation // arXiv preprint arXiv:1607.05368. 2016 Jun 19.
25. Devlin J., Chang M. W., Lee K., Toutanova K. Bert: Pretraining of deep bidirectional transformers for language understanding//arXiv preprint arXiv:1810.04805. 2018 Oct 11.
26. Sravanthi P., Srinivasu B. Semantic similarity between sentences // International Research Journal of Engineering and Technology (IRJET), 2017, vol. 4, no. 1, pp. 156-61.
27. F. Koto, M. Adriani, “The Use of POS Sequence for Analyzing Sentence Pattern in Twitter Sentiment Analysis,” Proceedings - IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2015, (March), 547–551 , 2015, doi:10.1109/WAINA.2015.58.
28. K. Gaydamaka, A. Belonogova. Applying Unsupervised Machine Learning Algorithms to Ensure Requirements Consistency // "Software Engineering" Vol. 13, No. 4, 2022.
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