Identifying Similar Test Cases That Are Specified in Natural Language
Bibliographic record
Abstract
In today's rapidly advancing software industry, we experience exciting technological growth every year in an extensive range of fields such as innovative AI-powered development, cloud and edge computing, machine learning, progressive and lightweight web and mobile applications etc. However, it is not quite relevant to the Software Testing industry. Most companies still rely on the outdated manual testing process despite the availability of Automation testing procedures. It may work for small teams testing the software using a limited number of test cases. Although, with the increase in team size and the number of test cases over time, the cost, effort and time needed to manually validate and review the test cases increase tenfold. It often leads to recurrent and unclear test cases in the test suite, delivered by different employees who often work across teams. These test cases are represented in Natural Language. Also, the redundant test cases can impact the manual testing process by testing the same feature multiple times and can reduce the possibility of writing multiple methods to test the same feature when automating tests in the future. Hence, in this project, we propose to address the problem of similar test cases using an unsupervised learning approach. Additionally, after removing redundancy in the test suite, we intend to identify key features in the software to be tested based on the description of the test cases in the suite, and group multiple test cases into a software feature. This feature can be directly assigned to a Quality Assurance engineer for testing.
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.
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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".