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
A good understanding of the impact of different types of bugs on various project aspects is essential to improve software quality research and practice. For instance, we would expect that security bugs are fixed faster than other types of bugs due to their critical nature. However, prior research has often treated all bugs as similar when studying various aspects of software quality (e.g., predicting the time to fix a bug), or has focused on one particular type of bug (e.g., security bugs) with little comparison to other types. In this paper, we study how different types of bugs (performance and security bugs) differ from each other and from the rest of the bugs in a software project. Through a case study on the Firefox project, we find that security bugs are fixed and triaged much faster, but are reopened and tossed more frequently. Furthermore, we also find that security bugs involve more developers and impact more files in a project. Our work is the first work to ever empirically study performance bugs and compare it to frequently-studied security bugs. Our findings highlight the importance of considering the different types of bugs in software quality research and practice.
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 | 0.000 |
| 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