The application of capture-recapture log-linear models to software inspections data
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
Re-inspection has been deployed in industry to improve the quality of software inspections. The number of remaining defects after inspection is an important factor affecting whether to re-inspect the document or not. Models based on capture-recapture (CR) sampling techniques have been proposed to estimate the number of defects remaining in the document after inspection. Several publications have studied the robustness of some of these models using software engineering data. Unfortunately, most of the existing studies did not examine the log linear models with respect software inspection data. In order o explore the performance of the log linear models, we evaluated their performance for three person inspection teams. Furthermore, we evaluated the models using an inspection data set that was previously used to asses different CR models. Generally speaking, the study provided very promising results. According to our results, the log linear models proved to be more robust that all CR based models previously assessed for three-person inspections.
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 | 0.000 | 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