Bibliographic record
Abstract
CHASE 2024 continues the tradition of a high-quality venue for research related to the cooperative and human aspects of software engineering.Researchers and practitioners have long recognized the need to investigate the cooperative and human aspects.However, their articles have been scattered across many conferences and communities.The CHASE conference provides academics and practitioners with a unified forum for discussing high-quality research studies, models, methods, and tools for human and cooperative aspects of software engineering.We are proud to present our exciting program for CHASE 2024, which includes great presentations and also offers plenty of opportunities for discussion.Our keynote speakers are Nicole Novielli, University of Bari, Italy, and Paul Ralph, Dalhousie University.The keynote speakers address some hot topics related to the human aspects of software engineering: emotion awareness in software development and the implications of realism (and philosophy of science) for human factors research.CHASE 2024 has received a good number of submissions across different tracks and accepted 20 highquality contributions from 58 original submissions.Of these, 13 are full papers and 7 are short papers.After desk rejections, 49 papers were sent out for review (short and full), of which 20 were accepted, resulting in an overall acceptance rate of 40.8%.This year, we have introduced a few novelties to CHASE.We have launched a partnership with ACM Transactions on Software Engineering and Methodology (TOSEM), one of the leading journals in software engineering, with two initiatives.The first initiative is a Journal-Fast Track for selected CHASE full papers.This option provides authors with an accelerated review process for their work in TOSEM, ensuring consistent reviewer feedback from the conference to the journal.The second initiative introduces a Journal-First Track for invited TOSEM papers on CHASE-related themes.This feature encourages a productive exchange between journal articles and conference presentations, thereby stimulating a more comprehensive conversation on key topics.These initiatives, collectively, aim to nurture collaboration, instigate wide-ranging discussions, and uphold the standards of high-quality research in both venues.We want to thank our devoted members of the CHASE organizing and program committee members for their support, allowing us to arrange and run an exciting conference and to assemble a high-quality program.We are also grateful for the encouragement, advice, and guidance of Teresa Baldassarre from i
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".