‹Programming› 2024
Mon 11 - Fri 15 March 2024 Lund, Sweden
Mon 11 Mar 2024 15:00 - 15:30 at M:G - Session IV

Programmers often benefit from the availability of concrete run-time data – examples – alongside abstract source code. However, programmers need to manually exercise the program to reach an interesting state or write code that reproducibly executes a functionality with concrete inputs to be able to observe concrete data.

In this work, we aim to automate this process by leveraging generative AI. We present a framework and a preliminary Smalltalk-based prototype that allows programmers to obtain and run examples for the currently viewed section of source code from a large language model.

Our approach demonstrates how locally-hosted LLMs can be fine-tuned and used for such a task with reasonable computational effort while minimizing common problems like hallucinations and out-of-date knowledge. The framework has direct applications in example-based live programming where it can suggest new examples, and in learning settings where novices need to know how certain functionality is being used.

Mon 11 Mar

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:00 - 17:00
Session IVPX/24 at M:G
Examples out of Thin Air: AI-generated Dynamic Context to Assist Program Comprehension by Example
Toni Mattis University of Potsdam; Hasso Plattner Institute, Eva Krebs Hasso Plattner Institute (HPI), University of Potsdam, Germany, Martin C. Rinard Massachusetts Institute of Technology, Robert Hirschfeld University of Potsdam; Hasso Plattner Institute
Developers' Perspective on Today's and Tomorrow's Programming Tool Assistance: A Survey
Peng Kuang Lund University, Sweden & WASP, Emma Söderberg Lund University, Martin Höst Lund University