Social Network Analysis in Software Testing to Categorize Unit Test Cases Based on Coverage Information
Bibliographic record
Abstract
Software testing is the most visible and cost-consuming activity in assuring the quality of software systems. In today's large-scale software systems, test (suite) maintenance is an inseparable part of software maintenance. Clarity of the purpose of each test case in the suite can be improved by proper naming and appropriate packaging, which decreases the cost of test maintenance. As a software system evolves its test suites need to be updated (maintained) to verify new or modified functionality of the software, and thus test cases may need to be categorized in a different way. In this work, we are proposing a technique to categorize the test cases automatically based on their coverage information. The proposed process can be performed dynamically over the life cycle of the system to improve the quality of test packaging. We build a social network of test cases and use coverage information to define links between them. This network is used to identify higher groups of test cases such as test package. To the best of our knowledge, this is the first trial in this direction. To evaluate our technique, we applied it on three open source systems with available JUnit test suits to identify test packages. We measured the quality of the discovered packages in terms of cohesion and coupling and compared them with the original packaging from test developers of these projects. The result shows that our technique can be used to categorize test cases automatically by even improving the quality of the packages.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".