Personal Recommendations in Requirements Engineering: The OpenReq Approach

  • Cristina PalomaresEmail author
  • Xavier Franch
  • Davide Fucci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10753)


[Context & motivation] Requirements Engineering (RE) is considered as one of the most critical phases in software development but still many challenges remain open. [Problem] Recommender systems have been applied to solve open RE challenges like requirements and stakeholder discovery; however, the existent proposals focus on specific RE tasks and do not give a general coverage for the RE process. [Principal ideas/results] In this research preview, we present the OpenReq approach to the development of intelligent recommendation and decision technologies that support different phases of RE in software projects. For doing so, the OpenReq approach will be formed by different parts that will be integrated in a process. Specifically, we present in this paper the OpenReq part for personal recommendations for stakeholders, which takes place during requirements elicitation, specification and analysis stages. [Contribution] OpenReq aims to improve and speed up RE processes, especially in large and distributed systems, by incorporating intelligent recommendation and decision technologies.


Recommender systems Personal recommendations Requirements Engineering 



The work presented in this paper has been conducted within the scope of the Horizon 2020 project OpenReq, which is supported by the European Union under the Grant Nr. 732463.


  1. 1.
    Hofmann, H., Lehner, F.: Requirements engineering as a success factor in software projects. IEEE Softw. 18(4), 58–66 (2001)CrossRefGoogle Scholar
  2. 2.
    Johann, T., Maalej, W.: Democratic mass participation of users in requirements engineering? In: RE 2015 (2015)Google Scholar
  3. 3.
    Davis, A.M.: The art of requirements triage. J. Comput. 36, 42–49 (2003)Google Scholar
  4. 4.
    Sikora, E., Tenbergen, B., Pohl, K.: Requirements engineering for embedded systems: an investigation of industry needs. In: Berry, D., Franch, X. (eds.) REFSQ 2011. LNCS, vol. 6606, pp. 151–165. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  5. 5.
    Méndez, D., Wagner, S.: Naming the pain in requirements engineering: a design for a global family of surveys and first results from Germany. In: IST (2015)Google Scholar
  6. 6.
    Mobasher, B., Cleland-Huang, J.: Recommender systems in requirements engineering. AI Mag. 32(3), 81–89 (2011)CrossRefGoogle Scholar
  7. 7.
    Hamza, M., Walker, R.J.: Recommending features and feature relationships from requirements documents for software product lines. In: RAISE 2015 (2015)Google Scholar
  8. 8.
    Elkamel, A., et al.: An UML class recommender system for software design. In: AICCSA 2016 (2016)Google Scholar
  9. 9.
    Intelligent Recommendation and Decision Technologies for Community-Driven Requirements Engineering (Horizon 2020 Project,
  10. 10.
    Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.): Recommendation Systems in Software Engineering. Springer, Heidelberg (2014). Google Scholar
  11. 11.
    Antoniol, G., Ayari, K., Di Penta, M., Khomh, F., Guéhéneuc, Y.-G.: Is it a bug or an enhancement?: a text-based approach to classify change requests. In: CASCON 2008 (2008)Google Scholar
  12. 12.
    Nagwani, N.K., Verma, S.: Predicting expert developers for newly reported bugs using frequent terms similarities of bug attributes. In: ICT-KE 2011 (2011)Google Scholar
  13. 13.
    Yu, L., Tsai, W.-T., Zhao, W., Wu, F.: Predicting defect priority based on neural networks. In: Cao, L., Zhong, J., Feng, Y. (eds.) ADMA 2010. LNCS (LNAI), vol. 6441, pp. 356–367. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  14. 14.
    Tu, Z., et al.: Gig services recommendation method for fuzzy requirement description. In: ICWS 2017 (2017)Google Scholar
  15. 15.
    Mens, K., Lozano, A.: Source code-based recommendation systems. In: Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering. LNCS (LNAI), pp. 93–130. Springer, Heidelberg (2014). CrossRefGoogle Scholar
  16. 16.
    Felfernig, A., et al.: An overview of recommender systems in requirements engineering. In: Maalej, W., Thurimella, A.K. (eds.) Managing Requirements Knowledge, pp. 315–332. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  17. 17.
    Roher, K., Richardson, D.: A proposed recommender system for eliciting software sustainability requirements. In: USER 2013 (2013)Google Scholar
  18. 18.
    Danylenko, A., Löwe, W.: Context-aware recommender systems for non-functional requirements. In: RSSE 2012 (2012)Google Scholar
  19. 19.
    Kumar, M., Ajmeri, N., Ghaisas, S.: Towards knowledge assisted agile requirements evolution. In: RSSE 2010 (2010)Google Scholar
  20. 20.
    Finkelstein, A., et al.: StakeRare: using social networks and collaborative filtering for large-scale requirements elicitation. IEEE Trans. Softw. Eng. 38, 707–735 (2012)CrossRefGoogle Scholar
  21. 21.
    Castro-Herrera, C., Cleland-Huang, J.: Utilizing recommender systems to support software requirements elicitation. In: RSSE 2010 (2010)Google Scholar
  22. 22.
    Felfernig, A., Friedrich, G., Schubert, M., Mandl, M., Mairitsch, M., Teppan, E.: Plausible repairs for inconsistent requirements. In: IJCAI 2009 (2009)Google Scholar
  23. 23.
    Cleland-Huang, J., Dumitru, H., Duan, C., Castro-Herrera, C.: Automated support for managing feature requests in open forums. Commun. ACM 52, 68–74 (2009)CrossRefGoogle Scholar
  24. 24.
    Garcia, J.E., Paiva, A.C.R.: REQAnalytics: a recommender system for requirements maintenance. Int. J. Softw. Eng. Appl. 10, 129–140 (2016)Google Scholar
  25. 25.
    Duan, C., Laurent, P., Cleland-Huang, J., Kwiatkowski, C.: Towards automated requirements prioritization and triage. Requirements Eng. 14, 73–89 (2009)CrossRefGoogle Scholar
  26. 26.
    Felfernig, A., Zehentner, C., Ninaus, G., Grabner, H., Maalej, W., Pagano, D., Weninger, L., Reinfrank, F.: Group decision support for requirements negotiation. In: Ardissono, L., Kuflik, T. (eds.) UMAP 2011. LNCS, vol. 7138, pp. 105–116. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  27. 27.
    Winkler, J., Vogelsang, A.: Automatic classification of requirements based on convolutional neural networks. In: REW 2016 (2016)Google Scholar
  28. 28.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014)Google Scholar
  29. 29.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, New York (2010)CrossRefGoogle Scholar
  30. 30.
    Falessi, D., Cantone, G., Canfora, G.: A comprehensive characterization of NLP techniques for identifying equivalent requirements. In: ESEM 2010 (2010)Google Scholar
  31. 31.
    Chien, J.T.: Hierarchical Theme and Topic Modeling. IEEE Trans. Neural Networks Learn. Syst. 27, 565–578 (2016)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Bucchiarone, A., Gnesi, S., Lami, G., Trentanni, G., Fantechi, A.: QuARS express - a tool demonstration. In: ASE 2008 (2008)Google Scholar
  33. 33.
    Rempel, P., Mäder, P.: Estimating the implementation risk of requirements in agile software development projects with traceability metrics. In: Fricker, S.A., Schneider, K. (eds.) REFSQ 2015. LNCS, vol. 9013, pp. 81–97. Springer, Cham (2015). Google Scholar
  34. 34.
    Said, A., Jain, B.J., Albayrak, S.: Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users. In: SAC 2012 (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.University of HamburgHamburgGermany

Personalised recommendations