Comparison of Approaches to Category Partition Specifications, Selection Criteria, and the Impact of the ‘Error’ and ‘Single’ Annotations using Industrial Case Studies
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
Given the significance of software testing, many methods such as black box testing have been introduced in order to make testing as efficient as possible. Experimental evidence is required to attest to the validity of such methods. In this thesis, we conducted experiments on twenty four test suites generated from two case studies targeting a real industrial system for generating financial reports. The experiments aim to evaluate the effectiveness of different Category Partition (CP) specifications on the same problem as well as that of the 'Single' and 'Error' constraints on the same CP specifications. The experiments also evaluate the effectiveness of the three selection criteria of Base-Choice, Each-Choice, and Pair-Wise. The effectiveness is measured in terms of cost, which is the number of generated test cases, fault detection of errors created by mutating code under test, and code coverage using Visual Studio.
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 | 0.001 |
| 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 | 0.000 | 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