Task-directed software inspection technique: an experiment and case study
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
Research in software inspection has led to the development of inspection techniques focused on providing structure and guidance to the individual inspector, with the goal of improving effectiveness. This paper defines and investigates a new inspection technique, task-directed inspection, specifically developed for inspecting complex computational code, but capable of being applied in other software domains. Students from the Royal Military College of Canada and Queen's University in Kingston, as participants in an experiment, applied two task directed techniques and an industry-standard non-structured inspection technique to a civil engineering code in use in military applications. Results from the experiment were analyzed with a new Orthogonal Defect Classification for computational code developed for this research. Based on this small sample group, the task-directed techniques help software inspectors more thoroughly examine and understand software code. This research also points out the differences between experienced and inexperienced inspectors, and opens up several possibilities for further research.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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