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SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks.

Mihaela Todorova Tomova1, Martin Hofmann1, Patrick Mäder1,2

  • 1Technische Universität Ilmenau, Ilmenau 98693, Germany.

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|May 11, 2022
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Summary
This summary is machine-generated.

We introduce the SEOSS-Queries dataset to address the scarcity of public data for text-to-SQL tasks in software development. This dataset aids in understanding and fulfilling diverse information needs, improving decision-making.

Keywords:
DatasetNatural language processingQuestion answeringSoftware and systems requirement engineeringText-to-SQL

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Area of Science:

  • Computer Science
  • Software Engineering
  • Natural Language Processing

Background:

  • Software development stakeholders require efficient access to information for decision-making.
  • Existing knowledge retrieval methods are time-consuming and require specialized expertise.
  • A scarcity of public datasets hinders the development of text-to-SQL benchmarks in this domain.

Purpose of the Study:

  • To introduce the SEOSS-Queries dataset, a novel resource for text-to-SQL tasks.
  • To facilitate the development and evaluation of models that translate natural language queries into SQL for software development information needs.
  • To provide a comprehensive benchmark for text-to-SQL research in software engineering.

Main Methods:

  • Collected natural language utterances and corresponding SQL queries from diverse sources.
  • Included data from previous studies, software projects, issue tracking tools, and expert surveys.
  • Developed a dataset comprising 1,162 English utterances, 166 SQL queries with varied precision, and 393,086 labeled issue tracker comments.

Main Results:

  • The SEOSS-Queries dataset offers a large-scale, diverse collection of software development-related information needs.
  • The dataset includes paraphrased utterances and SQL queries for broader applicability.
  • Pre-trained SQLNet and RatSQL models are provided for baseline comparisons.

Conclusions:

  • The SEOSS-Queries dataset addresses a critical need for public benchmarks in text-to-SQL for software engineering.
  • The dataset facilitates research on various NLP and database querying tasks.
  • The availability of this dataset and replication package will accelerate advancements in automated information retrieval for software development.