An Open Modern Software Testing Laboratory Courseware – An Experience Report
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
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.
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 | 0.000 |
| 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 | 0.002 | 0.002 |
| Open science | 0.004 | 0.001 |
| 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