Proceedings of the 8th international workshop on Software quality
Bibliographic record
Abstract
Software becomes ever more feature-rich and thereby harder to distinguish based on its functionality. Quality now differentiates between similar software products. Specifying, constructing, and assuring quality has been under research for several decades and continues to be a long-term research area, because of its many facets and its complexity. Current national and international initiatives show that there is an active research community in academia and industry. We are happy to welcome you the 8th International Workshop on Software Quality (WoSQ'11). The series of workshops on software quality are a forum to discuss and advance the state-of-the-art research and practice in software quality. This year's edition of the workshop is co-located with the joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering and brings some changes in the workshop format. We still have a keynote and paper presentations, but there is also a discussion session to bring up new topics and for the attendees to share their experiences. The call for papers attracted 11 submissions from Canada, Germany, Hungary, Italy, Japan, Turkey, the United Kingdom, and the United States. Out of these submissions, the program committee accepted 7 papers that cover a variety of topics, including data quality, quality models, in-process metrics and security analysis. In addition, the program includes a keynote talk by Motoei Azuma, emeritus professor at Waseda University, Tokyo, Japan.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.035 |
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".