Towards Automated Testing for Simple Programming Exercises

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Abstract

Automated feedback and grading platforms can require substantial effort when encoding new programming exercises for first-year students. Such exercises are usually simple but require defining several test cases to ensure their functional correctness. This paper describes our initial effort to leverage automated test case generation for simple programming exercises. We rely on grey-box fuzzing and random combinations of method calls to test the students' solutions and compare their execution to the results produced by a reference implementation. We implemented our approach in a pro- totype, called SimPyTest, openly available on GitHub. We discuss its usage and possible future extensions.
Original languageEnglish
Title of host publicationEASEAI 2022 - Proceedings of the 4th International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, co-located with ESEC/FSE 2022
EditorsAndreea Vescan, Camelia Serban, Julie Henry, Upsorn Praphamontripong
Place of PublicationSingapore
PublisherACM Press
Pages33-36
Number of pages4
ISBN (Electronic)9781450394536
DOIs
Publication statusPublished - 7 Nov 2022
Event4th International Workshop on Education through Advanced Software Engineering and Artificial Intelligence - Singapore, Singapore
Duration: 18 Nov 202218 Nov 2022
Conference number: 4
https://easeai.github.io

Publication series

NameEASEAI 2022 - Proceedings of the 4th International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, co-located with ESEC/FSE 2022

Workshop

Workshop4th International Workshop on Education through Advanced Software Engineering and Artificial Intelligence
Abbreviated titleEASEAI '22
Country/TerritorySingapore
CitySingapore
Period18/11/2218/11/22
Internet address

Keywords

  • programming education
  • automated software testing
  • fuzzing

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