[Context and Motivation:] In current practice, existing traceability data is often underutilized due to lack of accessibility and difficulties users have in constructing the complex SQL queries needed to address realistic Software Engineering questions. In our prior work we therefore presented TiQi --- a natural language NL interface for querying software projects. TiQi has been shown to transform a set of trace queries collected from IT experts at accuracy rates ranging from 47i¾?% to 93i¾?%. [Question/problem:] However, users need to quickly determine whether TiQi has correctly understood the NL query. [Principal ideas/results:] TiQi needs to communicate the transformed query back to the user and provide support for disambiguation and correction. In this paper we report on three studies we conducted to compare the effectiveness of four query representation techniques. [Contribution:] We show that simultaneously displaying a visual query representation, SQL, and a sample of the data results enabled users to most accurately evaluate the correctness of the transformed query.
['Sugandha Lohar', 'Jane Cleland-Huang', 'Alexander Rasin']
Evaluating the Interpretation of Natural Language Trace Queries