FORGE 2024
Sun 14 Apr 2024 Lisbon, Portugal
co-located with ICSE 2024

Code translation between programming languages is a long-existing and critical task in software engineering, facilitating the modernization of legacy systems, ensuring cross-platform compatibility, and enhancing software performance. With the recent advances in large language models (LLMs) and their applications to code translation, there is an increasing need for comprehensive evaluation of these models. In this study, we empirically analyze the generated outputs of eleven popular instruct-tuned LLMs with parameters ranging from 1B up to 46.7B on 3,820 translation pairs across five languages including C, C++, Go, Java, and Python. In our analysis, we found that between 26.4% and 73.7% of code translations produced by our evaluated LLMs necessitate post-processing. This is because these translations often include a mix of code, quotes, and text, rather than being purely source code. Overlooking the output format of these models can inadvertently lead to underestimation of their true performance. This is particularly evident when evaluating them with execution-based metrics such as Computational Accuracy (CA). Our research demonstrates a strategic combination of prompt engineering and regular expression usage that can effectively extract the source code from the model generation output. Results show that our method can help eleven selected models achieve an average Code Extraction Success Rate (CSR) of 92.73%. We believe our findings shed light and motivate future research in conducting more reliable benchmarks of LLMs for code translation.

Sun 14 Apr

Displayed time zone: Lisbon change

14:00 - 15:30
Keynote 2 & Properties of Foundation ModelsResearch Track / Keynotes at Luis de Freitas Branco
Chair(s): David Lo Singapore Management University, Feifei Niu Nanjing University
Keynote 2: Towards an Interpretable Science of Deep Learning for Software Engineering: A Causal Inference View
Denys Poshyvanyk William & Mary
Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code TranslationFull Paper
Research Track
Marcos Macedo Queen's University, Kingston, Ontario, Yuan Tian Queen's University, Kingston, Ontario, Filipe Cogo Huawei, Bram Adams Queen's University
Is Attention All You Need? Toward a Conceptual Model for Social Awareness in Large Language ModelsNew Idea Paper
Research Track
Gianmario Voria University of Salerno, Gemma Catolino University of Salerno, Fabio Palomba University of Salerno
An Exploratory Investigation into Code License Infringements in Large Language Model Training DatasetsFull Paper
Research Track
Jonathan Katzy Delft University of Technology, Răzvan Mihai Popescu Delft University of Technology, Arie van Deursen Delft University of Technology, Maliheh Izadi Delft University of Technology
Research Track