Abstract
[Context and motivation] Modern software engineering processes have shifted from traditional upfront requirements engineering (RE) to a more continuous way of conducting RE, particularly including data-driven approaches. [Question/problem] However, current research on data-driven RE focuses more on leveraging certain techniques such as natural language processing or machine learning than on making the concept fit for facilitating its use in the entire software development process. [Principal ideas/results] In this paper, we propose a research agenda composed of six distinct research directions. These include a data-driven RE infrastructure, embracing data heterogeneity, context-aware adaptation, data analysis and decision support, privacy and confidentiality, and finally process integration. Each of these directions addresses challenges that impede the broader use of data-driven RE. [Contribution] For researchers, our research agenda provides topics relevant to investigate. For practitioners, overcoming the underlying challenges with the help of the proposed research will allow to adopt a data-driven RE approach and facilitate its seamless integration into modern software engineering. For users, the proposed research will enable the transparency, control, and security needed to trust software systems and software providers.
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References
Brambilla, M., Cabot, J., Wimmer, M.: Model-driven Software Engineering in Practice, 2nd edn. Morgan & Claypool Publishers, San Rafael (2017)
Cabrera, O., Franch, X., Marco, J.: 3LConOnt: a three-level ontology for context modelling in context-aware computing. Softw. Syst. Model. 18(2), 1345–1378 (2017). https://doi.org/10.1007/s10270-017-0611-z
Dąbrowski, J., Letier, E., Perini, A., Susi, A.: Finding and analyzing app reviews related to specific features: a research preview. In: Knauss, E., Goedicke, M. (eds.) REFSQ 2019. LNCS, vol. 11412, pp. 183–189. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15538-4_14
Ebert, C., Heidrich, J., Martinez-Fernandez, S., Trendowicz, A.: Data science: technologies for better software. IEEE Softw. 36(6), 66–72 (2019)
Guzmán, L., Oriol, M., Rodríguez, P., Franch, X., Jedlitschka, A., Oivo, M.: How can quality awareness support rapid software development? – a research preview. In: Grünbacher, P., Perini, A. (eds.) REFSQ 2017. LNCS, vol. 10153, pp. 167–173. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54045-0_12
Jarke, M., Loucopoulos, P., Lyytinen, K., Mylopoulos, J., Robinson, W.: The brave new world of design requirements. Inf. Syst. 36(7), 992–1008 (2011)
Johanssen, J.O., Kleebaum, A., Bruegge, B., Paech, B.: How do practitioners capture and utilize user feedback during continuous software engineering? In: Proceedings of RE (2019)
Lindgren, E., Münch, J.: Raising the odds of success: the current state of experimentation in product development. Inf. Softw. Technol. 77, 80–91 (2016)
Maalej, W., Nayebi, M., Johann, T., Ruhe, G.: Toward data-driven requirements engineering. IEEE Softw. 33(1), 48–54 (2015)
Maalej, W., Nayebi, M., Ruhe, G.: Data-driven requirements engineering: an update. In: Proceedings of ICSE/SEIP, pp. 289–290. IEEE (2019)
Martínez-Fernández, S., et al.: Continuously assessing and improving software quality with software analytics tools: a case study. IEEE Access 7, 68219–68239 (2019)
Oriol, M., et al.: FAME: supporting continuous requirements elicitation by combining user feedback and monitoring. In: Proceedings of RE, pp. 217–227. IEEE (2018)
SonarQube: https://www.sonarqube.org. Accessed 24 Jan 2020
Vierhauser, M., Cleland-Huang, J., Burge, J., Grünbacher, P.: The interplay of design and runtime traceability for non-functional requirements. In: Proceedings of the 10th International Workshop on Software and Systems Traceability, pp. 3–10. IEEE (2019)
Villela, K., Groen, E.C., Doerr, J.: Ubiquitous requirements engineering: a paradigm shift that affects everyone. IEEE Softw. 36(2), 8–12 (2019)
Wüest, D., Fotrousi, F., Fricker, S.: Combining monitoring and autonomous feedback requests to elicit actionable knowledge of system use. In: Knauss, E., Goedicke, M. (eds.) REFSQ 2019. LNCS, vol. 11412, pp. 209–225. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15538-4_16
Acknowledgements
This work has been supported by: the Spanish project GENESIS (TIN2016-79269-R), the Christian Doppler Forschungsgesellschaft, the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Austrian Science Fund (FWF) under the grant numbers J3998-N31, P28519-N31, and P30525-N31.
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Franch, X. et al. (2020). Towards Integrating Data-Driven Requirements Engineering into the Software Development Process: A Vision Paper. In: Madhavji, N., Pasquale, L., Ferrari, A., Gnesi, S. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2020. Lecture Notes in Computer Science(), vol 12045. Springer, Cham. https://doi.org/10.1007/978-3-030-44429-7_10
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