Abstract
[Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman’s rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and categorization.
This work has been supported by and received funding from the ITEA3 XIVT, and KK Foundation’s ARRAY project.
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Notes
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The option “optimize for traceability” was selected in Embedded Coder.
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Xiao Han, https://github.com/hanxiao/bert-as-service.
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In our case, each folder for a pair contains two sub-folders with code of each requirement.
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RStudio, Available online, https://rstudio.com/.
- 7.
Replication package, https://doi.org/10.5281/zenodo.4275388.
References
Abbas, M., Jongeling, R., Lindskog, C., Enoiu, E.P., Saadatmand, M., Sundmark, D.: Product line adoption in industry: an experience report from the railway domain. In: Proceedings of the 24th ACM Conference on Systems and Software Product Line: Volume A - Volume A. SPLC 2020. ACM, New York (2020)
Abbas, M., Saadatmand, M., Enoiu, E., Sundamark, D., Lindskog, C.: Automated reuse recommendation of product line assets based on natural language requirements. In: Ben Sassi, S., Ducasse, S., Mili, H. (eds.) Reuse in Emerging Software Engineering Practices, pp. 173–189. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64694-3_11
Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F.: Automated extraction and clustering of requirements glossary terms. Trans. Soft. Eng. 43(10), 918–945 (2016)
Arora, C., Sabetzadeh, M., Goknil, A., Briand, L.C., Zimmer, F.: Change impact analysis for natural language requirements: an NLP approach. In: International Requirements Engineering Conference (RE), pp. 6–15. IEEE (2015)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Borg, M., Runeson, P., Ardö, A.: Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability. Empir. Softw. Eng. 19(6), 1565–1616 (2014). https://doi.org/10.1007/s10664-013-9255-y
Borg, M., Wnuk, K., Regnell, B., Runeson, P.: Supporting change impact analysis using a recommendation system: an industrial case study in a safety-critical context. IEEE Trans. Soft. Eng. 43(7), 675–700 (2016)
Castro-Herrera, C., Cleland-Huang, J., Mobasher, B.: Enhancing stakeholder profiles to improve recommendations in online requirements elicitation. In: International Requirements Engineering Conference, pp. 37–46. IEEE (2009)
Natt och Dag, J., Regnell, B., Gervasi, V., Brinkkemper, S.: A linguistic-engineering approach to large-scale requirements management. IEEE Softw. 22(1), 32–39 (2005)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dumitru, H., et al.: On-demand feature recommendations derived from mining public product descriptions. In: International Conference on Software Engineering, pp. 181–190 (2011)
Eyal-Salman, H., Seriai, A.D., Dony, C.: Feature-to-code traceability in a collection of software variants: combining formal concept analysis and information retrieval. In: 2013 IEEE 14th International Conference on Information Reuse & Integration (IRI), pp. 209–216 (2013)
Falessi, D., Cantone, G., Canfora, G.: Empirical principles and an industrial case study in retrieving equivalent requirements via natural language processing techniques. Trans. Softw. Eng. 39(1), 18–44 (2011)
Felfernig, A., Falkner, A., Atas, M., Franch, X., Palomares, C.: OpenReq: recommender systems in requirements engineering. In: RS-BDA, pp. 1–4 (2017)
Fernández, D.M., et al.: Naming the pain in requirements engineering. Empir. Softw. Eng. 22(5), 2298–2338 (2017)
Ferrari, A., Spagnolo, G.O., Gnesi, S.: Pure: a dataset of public requirements documents. In: 2017 IEEE 25th International Requirements Engineering Conference (RE), pp. 502–505 (2017). https://doi.org/10.1109/RE.2017.29
Gervasi, V., Zowghi, D.: Supporting traceability through affinity mining. In: International Requirements Engineering Conference (RE), pp. 143–152. IEEE (2014)
Guo, J., Cheng, J., Cleland-Huang, J.: Semantically enhanced software traceability using deep learning techniques. In: International Conference on Software Engineering (ICSE), pp. 3–14. IEEE (2017)
Hariri, N., Castro-Herrera, C., Cleland-Huang, J., Mobasher, B.: Recommendation systems in requirements discovery. In: Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 455–476. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5_17
Irshad, M., Petersen, K., Poulding, S.: A systematic literature review of software requirements reuse approaches. IST J. 93, 223–245 (2018)
Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. Royal Soc. A: Math. Phys. Eng. Sci. 374(2065), 20150202 (2016)
Krueger, C.W.: Easing the transition to software mass customization. In: van der Linden, F. (ed.) PFE 2001. LNCS, vol. 2290, pp. 282–293. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47833-7_25
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)
Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_3
Manning, C.D., Schütze, H., Raghavan, P.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)
Nyamawe, A.S., Liu, H., Niu, N., Umer, Q., Niu, Z.: Automated recommendation of software refactorings based on feature requests. In: International Requirements Engineering Conference (RE), pp. 187–198. IEEE (2019)
Palomares, C., Franch, X., Fucci, D.: Personal recommendations in requirements engineering: the OpenReq approach. In: Kamsties, E., Horkoff, J., Dalpiaz, F. (eds.) REFSQ 2018. LNCS, vol. 10753, pp. 297–304. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77243-1_19
Pohl, K., Böckle, G., van Der Linden, F.J.: Software Product Line Engineering: Foundations, Principles and Techniques. Springer, Heidelberg (2005)
Prechelt, L., Malpohl, G., Philippsen, M., et al.: Finding plagiarisms among a set of programs with JPlag. J. UCS 8(11), 1016 (2002)
Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50. ELRA, May 2010
Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.): Recommendation Systems in Software Engineering. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5
Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empir. Softw. Eng. 14(2), 131–164 (2009)
Shatnawi, A., Seriai, A., Sahraoui, H., Ziadi, T., Seriai, A.: Reside: reusable service identification from software families. JSS 170 (2020)
Shatnawi, A., Seriai, A.D., Sahraoui, H.: Recovering software product line architecture of a family of object-oriented product variants. J. Syst. Softw. 131, 325–346 (2017)
White, M., Tufano, M., Vendome, C., Poshyvanyk, D.: Deep learning code fragments for code clone detection. In: International Conference on Automated Software Engineering (ASE), pp. 87–98. IEEE (2016)
Wieringa, R., Daneva, M.: Six strategies for generalizing software engineering theories. Sci. Comput. Program. 101, 136–152 (2015)
Zhao, L., et al.: Natural language processing (NLP) for requirements engineering: A systematic mapping study. arXiv preprint arXiv:2004.01099 (2020)
Ziadi, T., Frias, L., da Silva, M.A.A., Ziane, M.: Feature identification from the source code of product variants. In: 2012 16th European Conference on Software Maintenance and Reengineering, pp. 417–422. IEEE (2012)
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Abbas, M., Ferrari, A., Shatnawi, A., Enoiu, E.P., Saadatmand, M. (2021). Is Requirements Similarity a Good Proxy for Software Similarity? An Empirical Investigation in Industry. In: Dalpiaz, F., Spoletini, P. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2021. Lecture Notes in Computer Science(), vol 12685. Springer, Cham. https://doi.org/10.1007/978-3-030-73128-1_1
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