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Automatic Generation of SQL Queries

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2014 ASEE Annual Conference & Exposition


Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014



Conference Session

Topics in Computing and Information Technologies

Tagged Division

Computing & Information Technology

Page Count


Page Numbers

24.221.1 - 24.221.11



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Paper Authors


Quan Do New Mexico State University

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Quan Do is currently pursuing her Ph.D. degree in Computer Science at New Mexico State University, Las Cruces, New Mexico. She received her M.S. In Information Systems and M.S. In Health Informatics from Marshall University, Huntington, WV. Her research interests are in automated SQL query generation, verification and validation of SQL queries, and Big Data Analytics.

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Rajeev K. Agrawal North Carolina A&T State University

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Dr. Rajeev Agrawal is an assistant professor in the department of computer systems technology at North Carolina A&T State University. He has published more than 40 referred journal and conference papers, and 4 book chapters. His current research focuses on Anomaly Detection in Computer Network, Big data Analytics, and Content-based Image Retrieval. He has also worked at HP Company in transportation, Medicaid Management Information System (MMIS) domains.

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Dhana Rao

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Dhana Rao is an Assistant Professor in Microbiology at Marshall University, West Virginia. She obtained her PhD in 2006 from the University of New South Wales, Australia. Her research interest are in metagenomics and bioinformatics

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Venkat N Gudivada Marshall University

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Venkat N Gudivada is a Professor and interim Chair of the of the Weisberg Division of Computer Science at Marshall University. He received his Ph.D. in Computer Science from the Center for Advanced Computer Studies, University of Louisiana at Lafayette. encompass Big Data Analytics, Verification and Validation of SQL Queries, HPC-driven applications, and Personalized eLearning.

His prior experience include work as a Vice President for Wall Street companies in New York City for over six years including Merrill Lynch and Financial Technologies International. Previous academic tenure include work at the University of Michigan, University of Missouri, and Ohio University.

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Automatic Generation of SQL QueriesAutomatic question generation is an active area of research. Its practical applications are many,especially in the context of learning assessment. In both online and traditional face-to-facecourses, learning outcomes are used as standards for measuring and comparing performance andachievement of learners. In a broader context, outcomes-based assessment is widely used inevaluating and improving academic degree programs. The former plays a central role especiallyin ABET accreditation of undergraduate engineering and computer science degree programs.Structured Query Language (SQL) is an ANSI and ISO standard declarative query language forquerying and manipulating relational databases. It is easy to write SQL queries but very difficultto validate them. Often students conclude that a SQL query is correct simply because the querycompiles, executes, and fetches data. Therefore, it is crucial that SQL assessment tasks arecarefully designed and implemented to insure a deep learning experience for students. In thispaper, we propose an approach to automatically generating SQL queries for assessing students'SQL learning. SQL concepts are modeled using RDFS. The user can select SQL concepts to beincluded in an assessment and our approach will generate appropriate queries. The proposedapproach is generic and is database metadata driven. A Web-based prototype system isdeveloped to illustrate the effectiveness of the proposed approach.The overarching goal for our work is driven by the following considerations: questions should begenerated on the fly, no question bank should be involved; answers to the questions should alsobe generated automatically; generated questions should feature all ANSI/ISO SQL concepts,individually or in permissible combinations; question generation system should work with anyrelational database created using any open source or commercial database management systems(that adhere to ANSI and ISO SQL standards); in addition to the generated questions andsolutions, the system should provide additional information to facilitate self-directed guidedlearning.

Do, Q., & Agrawal, R. K., & Rao, D., & Gudivada, V. N. (2014, June), Automatic Generation of SQL Queries Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20112

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