How Effective and Efficient are Student-Written Software Tests?
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
Many computer science students complete their undergraduate degrees with insufficient testing skills and knowledge. To understand the gaps in students' testing skills and knowledge, we analyzed 1014 software tests written by 12 groups in an undergraduate Software Quality Assurance (SQA) course project. In the project the student groups were provided a requirements document and were instructed to follow Test Driven Development (TDD) practices using black-box tests. To understand how the groups applied black-box testing in their project, we created an automatic tool to sort the tests into categories or "test buckets." By analyzing the test bucket data, we were able to assess the effectiveness and efficiency of student-written tests. We observed that the student groups were significantly more likely to test for explicit requirements than implicit requirements and significantly more likely to test happy paths than invalid inputs. Furthermore, students inefficiently tested happy paths, invalid inputs and explicit requirements resulting in a higher proportion of software tests with duplicate intent. Based on these results we provide insights into how black-box test education can be improved.
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 | 0.001 |
| 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 | 0.001 | 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