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Automated Requirement Dependency Analysis for Complex Technical Systems

Published online by Cambridge University Press:  26 May 2022

I. Gräßler,
C. Oleff,
M. Hieb and
D. Preuß
I. Gräßler
Affiliation:
Paderborn University, Germany
C. Oleff*
Affiliation:
Paderborn University, Germany
M. Hieb
Affiliation:
Paderborn University, Germany
D. Preuß
Affiliation:
Paderborn University, Germany
*
christian.oleff@hni.upb.de

Abstract

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Requirements changes are a leading cause for project failures. Due to propagation effects, change management requires dependency analysis. Existing approaches have shortcomings regarding ability to process large requirement sets, availability of required data, differentiation of propagation behavior and consideration of higher order dependencies. This paper introduces a new method for advanced requirement dependency analysis based on machine learning. Evaluation proves applicability and high performance by means of a case example, 4 development projects and 3 workshops with industry experts.

Keywords

requirements managementchange managementsystems engineering (SE)dependency analysisartificial intelligence (AI)

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022, pp. 1865 - 1874
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Abadi, A., Nisenson, M. and Simionovici, Y. (2008), “A Traceability Technique for Specifications”, in Krikhaar, R. (Ed.), 16th IEEE International Conference on Program Comprehension, 2008: ICPC 2008 ; 10 - 13 June 2008, Amsterdam, The Netherlands, 6/10/2008 - 6/13/2008, Amsterdam, IEEE, Piscataway, NJ, pp. 103–112.CrossRefGoogle Scholar
Alpaydın, E. (2019), Maschinelles Lernen, De Gruyter Studium, 2. Edition, De Gruyter Oldenbourg, Berlin.Google Scholar
Arslan, Y., Allix, K., Veiber, L., Lothritzm, Cedric, Bussyande, T., Klein, J. and Goujon, A. (2021), “A Comparison of Pre-Trained Language Models for Multi-Class Text Classification in the Financial Domain”, in Leskovec, J. (Ed.), Companion Proceedings of the Web Conference 2021, 19-23.04.2021, Ljubljana Slovenia, Association for Computing Machinery, New York, NJ, USA, pp. 260–268.Google Scholar
Atas, M., Samer, R. and Felfernig, A. (2018), “Automated Identification of Type-Specific Dependencies between Requirements”, in 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 12/3/2018 - 12/6/2018, Santiago, IEEE, Piscataway, NJ, pp. 688–695.CrossRefGoogle Scholar
Blessing, L.T.M. and Chakrabarti, A. (2009), DRM, a Design Research Methodology, 1. Edition, Springer London, Guildford, Surrey.Google Scholar
Borrull Baraut, R. (2019), “Incorporation of models in automatic requirement dependencies detection”, Master Thesis, Universitat Politècnica de Catalunya, 28 January.Google Scholar
Dahlstedt, Å.G. and Persson, A. (2005), “Requirements Interdependencies: State of the Art and Future Challenges”, in Aurum, A. and Wohlin, C. (Eds.), Engineering and Managing Software Requirements, Springer-Verlag Berlin Heidelberg, Berlin, Heidelberg, pp. 95–116.Google Scholar
Deshpande, G., Arora, C. and Ruhe, G. (2019), “Data-Driven Elicitation and Optimization of Dependencies between Requirements”, in Damian, D., Perini, A. and Lee, S.-W. (Eds.), 27th International Requirements Engineering Conference, 23-27 Nov. 2019, Jeju Island, Korea (South), IEEE, Piscataway, NJ, pp. 416–421.Google Scholar
Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. (2018), BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, available at: arXiv:1810.04805.Google Scholar
Di Thommazo, A., Ribeiro, T., Olivatto, G., Werneck, V. and Fabbri, S. (2013), “An Automatic Approach to Detect Traceability Links Using Fuzzy Logic”, in 27th Brazilian Symposium, pp. 21–30.Google Scholar
Giffin, M., de, Weck, O., Bounova, G., Keller, R., Eckert, C. and Clarkson, P.J. (2009), “Change Propagation Analysis in Complex Technical Systems”, Journal of Mechanical Design, Vol. 131 No. 8.CrossRefGoogle Scholar
Goknil, A., Kurtev, I., van den Berg, K. and Spijkerman, W. (2014), “Change impact analysis for requirements: A metamodeling approach”, Information and Software Technology, Vol. 56 No. 8, pp. 950–972.CrossRefGoogle Scholar
González-Carvajal, S. and Garrido-Merchán, E. (2020), Comparing BERT against traditional machine learning text classification, available at: http://arxiv.org/pdf/2005.13012v2.Google Scholar
Graessler, I., Preuß, D. and Oleff, C. (2020), “Automatisierte Identifikation und Charakterisierung von Anforderungsabhängigkeiten – Literaturstudie zum Vergleich von Lösungsansätzen”, in Krause, D., Paetzold, K. and Wartzack, S. (Eds.), Proceedings of the 31st Symposium Design for X (DFX2020), pp. 199–208.CrossRefGoogle Scholar
Gräßler, I. and Hentze, J. (2017), “Structuring and Describing Requirements in a Flexible Mesh for Development of Smart Interdisciplinary Systems”, in Araujo, A. and Mota Soares, C.A. (Eds.), Smart Structures and Materials, Springer International Publishing, Basel, pp. 1622–1631.Google Scholar
Gräßler, I., Oleff, C. and Preuß, D. (2021), “Holistic change propagation and impact analysis in requirements management”, in Wagner, B. and Wilson, J. (Eds.), Proceeding of R&D Management Conference 2021.Google Scholar
Gräßler, I., Oleff, C. and Scholle, P. (2020), “Method for Systematic Assessment of Requirement Change Risk in Industrial Practice”, Applied Sciences, Vol. 10 No. 23, p. 8697.CrossRefGoogle Scholar
Gräßler, I., Pottebaum, J. (2021), “Generic Product Lifecycle Model: A Holistic and Adaptable Approach for Multi-Disciplinary Product–Service Systems“, Applied Sciences, Vol. 11 No. 10, p. 4516.Google Scholar
Gräßler, I., Oleff, C. and Preuß, D. (2022), “Proactive Management of Requirement Changes in the Development of Complex Technical Systems“, Applied Sciences, Vol. 12 No. 4; p. 1874.Google Scholar
Gräßler, I., Pottebaum, J. (2022), “From Agile Strategic Foresight to Sustainable Mechatronic and Cyber-Physical Systems in Circular Economies“, in Krause, D. and Heyden, E. (Eds.), Design Methodology for Future Products, Springer International Publishing, Cham, pp. 3–26.Google Scholar
Hamdaqa, M. and Hamou-Lhadj, A. (2011), “An approach based on citation analysis to support effective handling of regulatory compliance”, Future Generation Computer Systems, Vol. 27 No. 4, pp. 395–410.CrossRefGoogle Scholar
Hamraz, B., Caldwell, N.H.M. and Clarkson, P.J. (2013), “A Holistic Categorization Framework for Literature on Engineering Change Management”, Systems Engineering, Vol. 16 No. 4, pp. 473–505.CrossRefGoogle Scholar
huggingface (2018), “bert-base-cased”, available at: huggingface.co/bert-base-cased (accessed 4 Nov. 2021).Google Scholar
huggingface (2020), “BERT Tokenizer”, available at: https://huggingface.co/transformers/main_classes/tokenizer.html (accessed 4 November 2021).Google Scholar
Hein, P. H. Kames, E., Chen, C. and Morkos, B. (2021), “Employing machine learning techniques to assess requirement change volatility“, in Research in Engineering Design, 32(2021) 2, pp. 245–269.Google Scholar
Jayatilleke, S. and Lai, R. (2018), “A systematic review of requirements change management”, Information and Software Technology, Vol. 93, pp. 163–185.Google Scholar
Koh, Y., Caldwell, M. and Clarkson, J. (2012), “A method to assess the effects of engineering change propagation”, Research in Engineering Design, Vol. 23 No. 4, pp. 329–351.CrossRefGoogle Scholar
Martinez, G.G., Carpio, A.F.D. and Gomez, L.N. (2019), “A Model for Detecting Conflicts and Dependencies in Non-Functional Requirements Using Scenarios and Use Cases”, in XLV Latin American Computing Conference (CLEI), Piscataway, NJ, IEEE, pp. 1–8.CrossRefGoogle Scholar
Mehr, M. R., Rashed, S. A. M., Lueder, A. and Mißler-Behr, M. (2021), “An Approach to Capture, Evaluate and Handle Complexity of Engineering Change Occurrences in New Product Development“, in International Journal of Industrial and Manufacturing Engineering, 15(2021) 9; pp. 400–408.Google Scholar
Misra, J. (2016), “Terminological inconsistency analysis of natural language requirements”, Information and Software Technology, Vol. 74, pp. 183–193.CrossRefGoogle Scholar
Morkos, B. (2012), “Computational representation and reasoning support for requirements change management in complex system design”, Dissertation, Clemson University, 2012.Google Scholar
Motger, Q., Borrull, R., Palomares, C. and Marco, J. (2019), OpenReq-DD: A requirements dependency detection tool, REFSQ Workshops, available at: ceur-ws.org/Vol-2376/NLP4RE19_paper01 (accessed 4 Nov. 2021).Google Scholar
Dag, Natt och, Regnell, J., Carlshamre, B., Andersson, P., and Karlsson, M., J. (2002), “A Feasibility Study of Automated Natural Language Requirements Analysis in Market-Driven Development”, Requirements Engineering, Vol. 7 No. 1, pp. 20–33.Google Scholar
Project, NLTK (2021), “Natural Language Toolkit”, available at: https://www.nltk.org/ (accessed 4 Nov. 2021).Google Scholar
Park, S., Kim, H., Ko, Y. and Seo, J. (2000), “Implementation of an efficient requirements-analysis supporting system using similarity measure techniques”, Information and Software Technology, Vol. 42, pp. 429–438.Google Scholar
Pohl, K. (1996), Process-centered requirements engineering, Advanced software development series, Vol. 5, Wiley; Research Studies Press, New York, NY, Taunton, Somerset, England.Google Scholar
Prabhu, S., Mohamed, M. and Misra, H. (2021), Multi-class Text Classification using BERT-based Active Learning, available at: http://arxiv.org/pdf/2104.14289v2.Google Scholar
Rao, D. and McMahan, B. (2020), Natural Language Processing mit PyTorch: Intelligente Sprachanwendungen mit Deep Learning erstellen, 1. Edition, O'Reilly Verlag, Heidelberg.Google Scholar
Reich, Y. and Barai, S.V. (1999), “Evaluating machine learning models for engineering problems”, Artificial Intelligence in Engineering, 13 (1999) 3, pp. 257–272.CrossRefGoogle Scholar
Samer, R., Stettinger, M., Atas, M., Felfernig, A., Ruhe, G. and Deshpande, G. (2019), “New Approaches to the Identification of Dependencies between Requirements”, in 31st International Conference on Tools with Artificial Intelligence, 4-6 Nov. 2019, Portland, OR, USA, IEEE, Piscataway, NJ, USA, pp. 1265–1270.Google Scholar
The Standish Group (2017), Chaos Manifesto 2018, West Yarmouth, USA.Google Scholar
Zhang, H., Li, J., Zhu, L., Jeffery, R., Liu, Y., Wang, Q. and Li, M. (2014), “Investigating dependencies in software requirements for change propagation analysis”, Information and Software Technology, Vol. 56, pp. 40–53.CrossRefGoogle Scholar
Zhu, X. and Jin, Z. (2005), “Inconsistency measurement of software requirements specifications: an ontology-based approach”, paper presented at 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'05), 16-20 June 2005, Shanghai, China.Google Scholar
Zichler, K. and Helke, S. (2017), “Ontologiebasierte Abhängigkeitsanalyse im Projektlastenheft”, in Dencker, P., Klenk, H., Keller, H.B. and Plödereder, E. (Eds.), Automotive - Safety & Security 2017: Sicherheit und Zuverlässigkeit für automobile Informationstechnik 30.-31. Mai 2017 Stuttgart, Germany, GI-Edition - lecture notes in informatics (LNI) Proceedings, Gesellschaft für Informatik e.V. (GI), Bonn, pp. 121–134.Google Scholar