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Detecting requirements defects with NLP patterns: an industrial experience in the railway domain

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Abstract

In the railway safety-critical domain requirements documents have to abide to strict quality criteria. Rule-based natural language processing (NLP) techniques have been developed to automatically identify quality defects in natural language requirements. However, the literature is lacking empirical studies on the application of these techniques in industrial settings. Our goal is to investigate to which extent NLP can be practically applied to detect defects in the requirements documents of a railway signalling manufacturer. To address this goal, we first identified a set of typical defects classes, and, for each class, an engineer of the company implemented a set of defect-detection patterns by means of the GATE tool for text processing. After a preliminary analysis, we applied the patterns to a large set of 1866 requirements previously annotated for defects. The output of the patterns was further inspected by two domain experts to check the false positive cases. Additional discard-patterns were defined to automatically remove these cases. Finally, SREE, a tool that searches for typically ambiguous terms, was applied to the requirements. The experiments show that SREE and our patterns may play complementary roles in the detection of requirements defects. This is one of the first works in which defect detection NLP techniques are applied on a very large set of industrial requirements annotated by domain experts. We contribute with a comparison between traditional manual techniques used in industry for requirements analysis, and analysis performed with NLP. Our experience shows that several discrepancies can be observed between the two approaches. The analysis of the discrepancies offers hints to improve the capabilities of NLP techniques with company specific solutions, and suggests that also company practices need to be modified to effectively exploit NLP tools.

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Notes

  1. https://github.com/ISTI-FMT/QUARS_plus_plus.

  2. The standard is currently replaced by ISO/IEC/IEEE 29148:2011 (ISO 2011).

  3. In this context, we consider as a pattern i also a dictionary from SREE-reduced, as defined in Section 3.4

  4. The dataset appears balanced since VE1 continued to randomly select new requirements from the original requirements considered, until a balanced number of accepted and rejected requirements was obtained.

  5. According to Landis and Koch (1977), the following qualitative measures are associated to the different ranges of the Cohen’s Kappa: k < 0, no agreement; 0 ≤ k ≤ 0.20, slight; 0.21 ≤ k ≤ 0.40, fair; 0.41 ≤ k ≤ 0.60, moderate; 0.61 ≤ k ≤ 0.80 substantial; and 0.81 ≤ k ≤ 1 almost perfect agreement.

  6. The results presented in Tables 9 and 8 differ from those presented in our original conference paper. When VE2 replicated the experiments performed by VE1, discrepancies in the results emerged. These were traced back to the usage of a support tool, developed by VE1 on top of GATE, to ease the analysis of the requirements. The tool introduced further manipulations, which led to incorrect numerical results. The results presented in this paper are produced based solely on the analysis of the output of GATE, and are, to the best of our knowledge, correct.

  7. The requirement was not rejected since it was clarified by other subsequent requirements. This violates the guideline (c) that require requirements to be stand-alone, but the defect was not considered crucial.

  8. The value of pR that considers the analysis of the false positive cases for the SREE dictionaries cannot be provided, since we analysed only a subset of the defects for the plurals class. However, the average value of pD gives a clear indication of the precision of SREE at the level of defects.

  9. https://gate.ac.uk/commercial.html.

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Acknowledgments

The authors would like to thank the anonymous reviewers for their precious recommendations, which contributed to make the paper clearer and more complete. We are also extremely grateful to Daniel M. Berry, for providing the dictionaries of SREE, and to Daniel Méndez Fernández for his valuable suggestions on reporting case studies in software engineering. This work has been partially funded by the ASTRail project. This project received funding from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777561. The content of this paper reflects only the authors’ view and the Shift2Rail Joint Undertaking is not responsible for any use that may be made of the included information.

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Authors and Affiliations

  1. ISTI-CNR, Via G. Moruzzi, 1, 56124, Pisa, Italy

    Alessio Ferrari & Stefania Gnesi

  2. Dipartimento di Ingegneria dell’Informazione, University of Florence, Via Santa Marta, 3, 50139, Florence, Italy

    Gloria Gori & Alessandro Fantechi

  3. Alstom Signalling Solutions, Via Pietro Fanfani, 21, 50127, Florence, Italy

    Benedetta Rosadini, Iacopo Trotta & Stefano Bacherini

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  1. Alessio Ferrari
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  2. Gloria Gori
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  3. Benedetta Rosadini
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  4. Iacopo Trotta
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  6. Alessandro Fantechi
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  7. Stefania Gnesi
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Correspondence to Alessio Ferrari.

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Communicated by: Anna Perini and Paul Grünbacher

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Ferrari, A., Gori, G., Rosadini, B. et al. Detecting requirements defects with NLP patterns: an industrial experience in the railway domain. Empir Software Eng 23, 3684–3733 (2018). https://doi.org/10.1007/s10664-018-9596-7

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