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Estimating the number of remaining links in traceability recovery

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Abstract

Although very important in software engineering, establishing traceability links between software artifacts is extremely tedious, error-prone, and it requires significant effort. Even when approaches for automated traceability recovery exist, these provide the requirements analyst with a, usually very long, ranked list of candidate links that needs to be manually inspected. In this paper we introduce an approach called Estimation of the Number of Remaining Links (ENRL) which aims at estimating, via Machine Learning (ML) classifiers, the number of remaining positive links in a ranked list of candidate traceability links produced by a Natural Language Processing techniques-based recovery approach. We have evaluated the accuracy of the ENRL approach by considering several ML classifiers and NLP techniques on three datasets from industry and academia, and concerning traceability links among different kinds of software artifacts including requirements, use cases, design documents, source code, and test cases. Results from our study indicate that: (i) specific estimation models are able to provide accurate estimates of the number of remaining positive links; (ii) the estimation accuracy depends on the choice of the NLP technique, and (iii) univariate estimation models outperform multivariate ones.

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Notes

  1. http://www.finmeccanica.com/en/home

  2. http://www.cs.wm.edu/semeru/tefse2011/Challenge.htm

  3. http://www.ing.unisannio.it/mdipenta/estimating-links.tgz

  4. https://github.com/apicchiani/estimationTool

  5. For LSA the integer after the technique, e.g., LSA 100, indicates the number of LSA concepts.

  6. http://www.falessi.com/EMSE-Traceability2016.zip

  7. This is a common problem in IR-based traceability link recovery (De Lucia et al. 2011).

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Authors and Affiliations

  1. Department of Computer Science, California Polytechnic State University, San Luis Obispo, CA, USA

    Davide Falessi

  2. Department of Engineering, University of Sannio, Benevento, BN, Italy

    Massimiliano Di Penta & Gerardo Canfora

  3. Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, DICII, Rome, Italy

    Giovanni Cantone

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  1. Davide Falessi
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  2. Massimiliano Di Penta
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  3. Gerardo Canfora
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  4. Giovanni Cantone
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Correspondence to Davide Falessi.

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Communicated by: Patrick Mäder, Rocco Oliveto and Andrian Marcus

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Falessi, D., Di Penta, M., Canfora, G. et al. Estimating the number of remaining links in traceability recovery. Empir Software Eng 22, 996–1027 (2017). https://doi.org/10.1007/s10664-016-9460-6

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