GitHubCopilot is an artificial intelligence tool for automatically generating source code from natural language problem descriptions. Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code. Prior work exploring OpenAI Codex, the underlying model that powers Copilot, has shown it performs well on typical CS1 problems thus raising concerns about its potential impact on how introductory programming courses are taught. However, little is known about the types of problems for which Copilot does not perform well, or about the natural language interactions that a student might have with Copilot when resolving errors. We explore these questions by evaluating the performance of Copilot on a publicly available dataset of 166 programming problems. We find that it successfully solves around half of these problems on its very first attempt, and that it solves 60% of the remaining problems using only natural language changes to the problem description. We argue that this type of prompt engineering, which we believe will become a standard interaction between human and Copilot when it initially fails, is a potentially useful learning activity that promotes computational thinking skills, and is likely to change the nature of code writing skill development.
CCS CONCEPTS• Social and professional topics → Computing education; CS1; • Computing methodologies → Artificial intelligence.
The event-driven programming pattern is pervasive in a wide range of modern software applications. Unfortunately, it is not easy to achieve good performance and responsiveness when developing event-driven applications. Traditional approaches require a great amount of programmer effort to restructure and refactor code, to achieve the performance speedup from parallelism and asynchronization. Not only does this restructuring require a lot of development time, it also makes the code harder to debug and understand. We propose an asynchronous programming model based on the philosophy of OpenMP, which does not require code restructuring of the original sequential code. This asynchronous programming model is complementary to the existing OpenMP fork-join model. The coexistence of the two models has potential to decrease developing time for parallel event-driven programs, since it avoids major code refactoring. In addition to its programming simplicity, evaluations show that this approach achieves good performance improvements consistent with more traditional event-driven parallelization.
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