SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks
Stakeholders of software development projects have various information needs for making rational decisions during their daily work. Satisfying these needs requires substantial knowledge of where and how the relevant information is stored and consumes valuable time that is often not available. Easing...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340922004152 |
| _version_ | 1829462099650150400 |
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| author | Mihaela Todorova Tomova Martin Hofmann Patrick Mäder |
| author_facet | Mihaela Todorova Tomova Martin Hofmann Patrick Mäder |
| author_sort | Mihaela Todorova Tomova |
| collection | DOAJ |
| description | Stakeholders of software development projects have various information needs for making rational decisions during their daily work. Satisfying these needs requires substantial knowledge of where and how the relevant information is stored and consumes valuable time that is often not available. Easing the need for this knowledge is an ideal text-to-SQL benchmark problem, a field where public datasets are scarce and needed. We propose the SEOSS-Queries dataset consisting of natural language utterances and accompanying SQL queries extracted from previous studies, software projects, issue tracking tools, and through expert surveys to cover a large variety of information need perspectives. Our dataset consists of 1,162 English utterances translating into 166 SQL queries; each query has four precise utterances and three more general ones. Furthermore, the dataset contains 393,086 labeled utterances extracted from issue tracker comments. We provide pre-trained SQLNet and RatSQL baseline models for benchmark comparisons, a replication package facilitating a seamless application, and discuss various other tasks that may be solved and evaluated using the dataset. The whole dataset with paraphrased natural language utterances and SQL queries is hosted at figshare.com/s/75ed49ef01ac2f83b3e2. |
| first_indexed | 2024-12-12T12:02:38Z |
| format | Article |
| id | doaj.art-45d12762121f4d7e8af0ba71c009a3cc |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-12T12:02:38Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-45d12762121f4d7e8af0ba71c009a3cc2022-12-22T00:25:04ZengElsevierData in Brief2352-34092022-06-0142108211SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasksMihaela Todorova Tomova0Martin Hofmann1Patrick Mäder2Corresponding author.; Technische Universität Ilmenau, Ilmenau 98693, GermanyTechnische Universität Ilmenau, Ilmenau 98693, GermanyTechnische Universität Ilmenau, Ilmenau 98693, Germany; Faculty of Biological Sciences, Friedrich Schiller University, Jena 07745, GermanyStakeholders of software development projects have various information needs for making rational decisions during their daily work. Satisfying these needs requires substantial knowledge of where and how the relevant information is stored and consumes valuable time that is often not available. Easing the need for this knowledge is an ideal text-to-SQL benchmark problem, a field where public datasets are scarce and needed. We propose the SEOSS-Queries dataset consisting of natural language utterances and accompanying SQL queries extracted from previous studies, software projects, issue tracking tools, and through expert surveys to cover a large variety of information need perspectives. Our dataset consists of 1,162 English utterances translating into 166 SQL queries; each query has four precise utterances and three more general ones. Furthermore, the dataset contains 393,086 labeled utterances extracted from issue tracker comments. We provide pre-trained SQLNet and RatSQL baseline models for benchmark comparisons, a replication package facilitating a seamless application, and discuss various other tasks that may be solved and evaluated using the dataset. The whole dataset with paraphrased natural language utterances and SQL queries is hosted at figshare.com/s/75ed49ef01ac2f83b3e2.http://www.sciencedirect.com/science/article/pii/S2352340922004152Software and systems requirement engineeringText-to-SQLDatasetQuestion answeringNatural language processing |
| spellingShingle | Mihaela Todorova Tomova Martin Hofmann Patrick Mäder SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks Data in Brief Software and systems requirement engineering Text-to-SQL Dataset Question answering Natural language processing |
| title | SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks |
| title_full | SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks |
| title_fullStr | SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks |
| title_full_unstemmed | SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks |
| title_short | SEOSS-Queries - a software engineering dataset for text-to-SQL and question answering tasks |
| title_sort | seoss queries a software engineering dataset for text to sql and question answering tasks |
| topic | Software and systems requirement engineering Text-to-SQL Dataset Question answering Natural language processing |
| url | http://www.sciencedirect.com/science/article/pii/S2352340922004152 |
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