Proceedings of the 2008 workshop on Defects in large software systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Welcome to DEFECTS 2008. We are delighted to present a selection of excellent papers focusing on defects in large software systems. The eight accepted technical papers mostly cover topics such as defect detection, defect prediction and mining software repositories, but also usability aspects of static analysis tools. The workshop will also feature a thought-provoking keynote and four short papers. In addition to the technical program, the workshop hosted a defect challenge, in which researchers were encouraged to benchmark their favorite static or dynamic defect localization approach. Find for a given failure, the location of the defect automatically. The test defects were a subset of real defects from the AspectJ program (collected by the iBugs project at Saarland University). Unfortunately, the challenge did not receive any submissions---maybe the challenge problem was too big or the timeframe too short. In any case, we will try to establish an improved defect challenge at a future venue.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it