Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1
Bibliographic record
Abstract
We are delighted to welcome you to the 32nd ACM/IEEE International Conference on Software Engineering, in Cape Town, South Africa. This is a very special occasion, as it is the first time that ICSE has been held in Africa. This promises to be a particularly exciting event, with an opening address by the Nobel Laureate, the Most Reverend Bishop Desmond Tutu, keynotes by Sir David King, Professor Fred Schneider and Clem Sunter, and an outstanding technical program. In keeping with ICSE's first time in Africa, the theme of the conference is New Horizons. New challenges face our community: from new hardware platforms, increased software complexity and more demanding safety and dependability requirements to major global issues such as the environment and the economic downturn. Our community can play a critical role in meeting these challenges, and the conference provides an ideal forum to discuss and report on new challenges and promising results in software engineering. The long and distinguished tradition of ICSE continues this year with a broad spectrum of events showcasing the best current work in research, practice and education. The heart of the meeting is the research paper track. This year, this track received 380 submissions. These submissions underwent a rigorous two-phase review processes, including for the first time an opportunity for authors to respond to the reviewers for consideration at the final program committee meeting. After a two-day meeting at Imperial College in London, a 40-member program committee selected 54 papers (14%) for inclusion in the program. Papers present research from many different countries, including: Belgium, Brazil, Canada, China, France, Germany, Greece, India, Italy, Japan, New Zealand, Singapore, Switzerland, Sweden, the UK, and the USA. The conference also includes the following tracks: Software Engineering in Practice, Software Engineering in Education, New Ideas and Emerging Results, and Research Demonstrations. In each case, the numbers of accepted papers and submissions were respectively 16 from 71 (22.5%), 7 from 33 (21%), 19 from 76 (25%) and 15 from 60 (25%). Together with the research track, this represents a total of 620 paper submissions, a wonderful indication of the continuing strength of ICSE! In addition, we have a large number of workshops and tutorials both before and after the conference, and a Doctoral Symposium and Emerging Faculty Symposium.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.161 | 0.103 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".