Maybe you wrote that code and maybe you didn’t. If AI helped you, such as the OpenAI Codex in GitHub Copilot, how did it solve your problem? How much did Artificial Intelligence help or hinder your solution? Join us to discuss a paper by Michel Wermelinger from the Open University published in the SIGCSE technical symposium earlier this month on this very topic.  We’ll be joined by Michel who will present a lightning talk to kick-off our discussion. Here’s the abstract of his paper:
The teaching and assessment of introductory programming involves writing code that solves a problem described by text. Previous research found that OpenAI’s Codex, a natural language machine learning model trained on billions of lines of code, performs well on many programming problems, often generating correct and readable Python code. GitHub’s version of Codex, Copilot, is freely available to students. This raises pedagogic and academic integrity concerns. Educators need to know what Copilot is capable of, in order to adapt their teaching to AI-powered programming assistants. Previous research evaluated the most performant Codex model quantitatively, e.g. how many problems have at least one correct suggestion that passes all tests. Here I evaluate Copilot instead, to see if and how it differs from Codex, and look qualitatively at the generated suggestions, to understand the limitations of Copilot. I also report on the experience of using Copilot for other activities asked of students in programming courses: explaining code, generating tests and fixing bugs. The paper concludes with a discussion of the implications of the observed capabilities for the teaching of programming.
Michel Wermelinger (2023) Using GitHub Copilot to Solve Simple Programming Problems in Proceedings of the 54th ACM Technical Symposium on Computer Science Education Pages SIGCSE 2023 page 172–178 DOI: 10.1145/3545945.3569830
Automatic code generators have been with us a while, but how do modern AI powered bots perform on introductory programming assignments? Join us to discuss the implications of the OpenAI Codex on introductory programming courses on Monday 4th July at 2pm BST. We’ll be discussing a paper by James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly and James Prather  for our monthly SIGCSE journal club meetup on zoom. Here is the abstract:
Recent advances in artificial intelligence have been driven by an exponential growth in digitised data. Natural language processing, in particular, has been transformed by machine learning models such as OpenAI’s GPT-3 which generates human-like text so realistic that its developers have warned of the dangers of its misuse. In recent months OpenAI released Codex, a new deep learning model trained on Python code from more than 50 million GitHub repositories. Provided with a natural language description of a programming problem as input, Codex generates solution code as output. It can also explain (in English) input code, translate code between programming languages, and more. In this work, we explore how Codex performs on typical introductory programming problems. We report its performance on real questions taken from introductory programming exams and compare it to results from students who took these same exams under normal conditions, demonstrating that Codex outscores most students. We then explore how Codex handles subtle variations in problem wording using several published variants of the well-known “Rainfall Problem” along with one unpublished variant we have used in our teaching. We find the model passes many test cases for all variants. We also explore how much variation there is in the Codex generated solutions, observing that an identical input prompt frequently leads to very different solutions in terms of algorithmic approach and code length. Finally, we discuss the implications that such technology will have for computing education as it continues to evolve, including both challenges and opportunities. (see accompanying slides and sigarch.org/coping-with-copilot/)
James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, James Prather (2022) The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming ACE ’22: Australasian Computing Education Conference Pages 10–19 DOI:10.1145/3511861.3511863