An Open Modern Software Testing Laboratory Courseware – An Experience Report
Bibliographic record
Abstract
In order to effectively teach software testing students how to test real-world software, the software tools, exercises, and lab projects chosen by testing educators should be practical and realistic. However, there are not many publicly-available realistic testing courseware for software testing educators to adapt and customize. Even for the existing testing lab exercises developed and/or used by the educators, there are various drawbacks, e.g.: (1) They are not usually kept up-to-date with the most recent testing tools and technologies, e.g., JUnit, (2) They are not built based on realistic/real-world Systems Under Test (SUTs), but rather use ¿toy¿ examples (SUTs). The above needs were the main motives for the author and his team at the University of Calgary to modernize the lab exercises of an undergraduate software testing course. This paper presents the designed lab courseware, and the experiences learned from using the courseware in the University of Calgary. It is hoped (and expected) that other software testing educators start to use this laboratory courseware and find it useful for their instruction and training needs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.020 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".