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Is Requirements Similarity a Good Proxy for Software Similarity? An Empirical Investigation in Industry

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Requirements Engineering: Foundation for Software Quality (REFSQ 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12685))

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

  1. 1.

    The option “optimize for traceability” was selected in Embedded Coder.

  2. 2.

    https://spacy.io/.

  3. 3.

    https://github.com/RaRe-Technologies/gensim-data.

  4. 4.

    Xiao Han, https://github.com/hanxiao/bert-as-service.

  5. 5.

    In our case, each folder for a pair contains two sub-folders with code of each requirement.

  6. 6.

    RStudio, Available online, https://rstudio.com/.

  7. 7.

    Replication package, https://doi.org/10.5281/zenodo.4275388.

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Author information

Authors and Affiliations

  1. RISE Research Institutes of Sweden, Västerås, Sweden

    Muhammad Abbas & Mehrdad Saadatmand

  2. Mälardalens University, Västerås, Sweden

    Muhammad Abbas & Eduard Paul Enoiu

  3. CNR-ISTI, Pisa, Italy

    Alessio Ferrari

  4. Berget-Levrault, Montpellier, France

    Anas Shatnawi

Authors
  1. Muhammad Abbas
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  2. Alessio Ferrari
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  3. Anas Shatnawi
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  4. Eduard Paul Enoiu
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  5. Mehrdad Saadatmand
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Corresponding author

Correspondence to Muhammad Abbas .

Editor information

Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Kennesaw State University, Kennesaw, GA, USA

    Paola Spoletini

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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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  • DOI: https://doi.org/10.1007/978-3-030-73128-1_1

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