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TiQi: answering unstructured natural language trace queries

  • RE 2014
  • Published:
Requirements Engineering Aims and scope Submit manuscript

Abstract

Software traceability is a required element in the development and certification of safety-critical software systems. However, trace links, which are created at significant cost and effort, are often underutilized in practice due primarily to the fact that project stakeholders often lack the skills needed to formulate complex trace queries. To mitigate this problem, we present a solution which transforms spoken or written natural language queries into structured query language (SQL). TiQi includes a general database query mechanism and a domain-specific model populated with trace query concepts, project-specific terminology, token disambiguators, and query transformation rules. We report results from four different experiments exploring user preferences for natural language queries, accuracy of the generated trace queries, efficacy of the underlying disambiguators, and stability of the trace query concepts. Experiments are conducted against two different datasets and show that users have a preference for written NL queries. Queries were transformed at accuracy rates ranging from 47 to 93 %.

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Acknowledgments

The work described in this paper was partially funded by NSF Grant CCF-1319680.

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Authors and Affiliations

  1. DePaul University, Chicago, USA

    Piotr Pruski, Sugandha Lohar, William Goss, Alexander Rasin & Jane Cleland-Huang

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  1. Piotr Pruski
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  2. Sugandha Lohar
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Correspondence to Jane Cleland-Huang.

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Pruski, P., Lohar, S., Goss, W. et al. TiQi: answering unstructured natural language trace queries. Requirements Eng 20, 215–232 (2015). https://doi.org/10.1007/s00766-015-0224-4

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