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On the relationship between similar requirements and similar software: A case study in the railway domain
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems. (Smart Industrial Automation)ORCID iD: 0000-0001-6418-9971
CNR-ISTI, Italy.
Berger-Levrault, France.
Mälardalen University, Sweden.
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2023 (English)In: Requirements Engineering, ISSN 0947-3602, E-ISSN 1432-010X, Vol. 28, p. 23-47Article in journal (Refereed) Epub ahead of print
Abstract [en]

Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. 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 change impact analysis.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. Vol. 28, p. 23-47
Keywords [en]
Correlation, Language models, Perception of similarity, Requirements similarity, Software similarity, Codes (symbols), Computer software reusability, Deep learning, Railroads, Recommender systems, Semantics, Software testing, Case-studies, Code retrievals, Industrial programs, Language model, Processing approach, Requirement similarities, Similarity computation, Software similarities, Natural language processing systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-58532DOI: 10.1007/s00766-021-00370-4Scopus ID: 2-s2.0-85123067513OAI: oai:DiVA.org:ri-58532DiVA, id: diva2:1638699
Note

 Funding text 1: This work has been supported by and received funding from the ITEA3 XIVT, and KK Foundation’s ARRAY project.

Available from: 2022-02-17 Created: 2022-02-17 Last updated: 2023-10-05Bibliographically approved

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Abbas, MuhammadSaadatmand, Mehrdad

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