Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering
Bibliographic record
Abstract
on Software Engineering (ICSE 2024).FORGE aims to bring researchers, practitioners, and educators from the AI and Software Engineering community to solve the new challenges we meet in the era of foundation models.Foundation models (e.g., ChatGPT and Llama) have attracted great attention from both academia and industry.In Software Engineering, several studies showed that Large-Language Models (LLMs) achieved remarkable performance in various tasks, including code generation, testing, code review, and program repair.Recently, many LLM-based development tools have been released to improve software development and show great potential, for example, GitHub Copilot and Amazon CodeWhisperer.This year, in the first edition of FORGE, we received 32 submissions, including 16 full research papers and 16 new idea papers.The papers have been reviewed by 73 program committee members, and we ensure that each paper received at least five reviews, to ensure plenty of feedback.Reviews were thoroughly discussed by the PC members, and, in the end, we accepted 15 papers, which included 8 technical papers (50% acceptance rate) and 7 new idea papers (44% acceptance rate).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".