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An NLP approach for cross-domain ambiguity detection in requirements engineering

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Abstract

During requirements elicitation, different stakeholders with diverse backgrounds and skills need to effectively communicate to reach a shared understanding of the problem at hand. Linguistic ambiguity due to terminological discrepancies may occur between stakeholders that belong to different technical domains. If not properly addressed, ambiguity can create frustration and distrust during requirements elicitation meetings, and lead to problems at later stages of development. This paper presents a natural language processing approach to identify ambiguous terms between different domains, and rank them by ambiguity score. The approach is based on building domain-specific language models, one for each stakeholders’ domain. Word embeddings from each language model are compared in order to measure the differences of use of a term, thus estimating its potential ambiguity across the domains of interest. We evaluate the approach on seven potential elicitation scenarios involving five domains. In the evaluation, we compare the ambiguity rankings automatically produced with the ones manually obtained by the authors as well as by multiple annotators recruited through Amazon Mechanical Turk. The rankings produced by the approach lead to a maximum Kendall’s Tau of 88%. However, for several elicitation scenarios, the application of the approach was unsuccessful in terms of performance. Analysis of the agreement among annotators and of the observed inaccuracies offer hints for further research on the relationship between domain knowledge and natural language ambiguity.

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Notes

  1. https://doi.org/10.5281/zenodo.1476902.

  2. https://en.wikipedia.org/wiki/Category:Computer_science.

  3. In the pseudo-code, array indexes start from 1.

  4. When n = 2, the variance is equivalent to the mean squared error.

  5. https://pypi.org/project/wikipedia/.

  6. https://radimrehurek.com/gensim/.

  7. https://spacy.io.

  8. spaCy is used also to identify nouns through POS tagging. The authors of spaCy (Honnibal and Montani 2017) report a 97% accuracy for the pretrained POS tagger for English, which is in line with the state of the art (Manning 2011). Given the high accuracy, we consider the influence of POS tagger errors as negligible.

  9. https://doi.org/10.5281/zenodo.1476902.

  10. In this preliminary task, the authors annotated a group of 60 sentences for the (I3) Medical Software (CS, MED) and 60 sentences for the (M2) Medical Robot scenario (CS, EEN, MEN, MED)—the sentence sets were not included in the evaluation presented in this work. The annotation was followed by a discussion on the annotated data and evident cases of disagreement, which allowed the authors to have an initial common ground to select the examples.

  11. The original Kendall’s Tau measure does not provide a policy to handle ties.

  12. https://arxiv.org.

  13. https://www.qualicen.de/en/.

  14. https://qracorp.com.

  15. https://wordnet.princeton.edu.

  16. Preliminary experiments were performed in this direction, to obtain the currently adopted parameters’ values. However, we reckon that a systematic evaluation campaign is required, as there may be an optimal parameters’ selection for each domain group.

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    Alessio Ferrari & Andrea Esuli

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Ferrari, A., Esuli, A. An NLP approach for cross-domain ambiguity detection in requirements engineering. Autom Softw Eng 26, 559–598 (2019). https://doi.org/10.1007/s10515-019-00261-7

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