Prioritizing Manual Test Cases in Traditional and Rapid Release Environments
Bibliographic record
Abstract
Test case prioritization is one of the most practically useful activities in testing, specially for large scale systems. The goal is ranking the existing test cases in a way that they detect faults as soon as possible, so that any partial execution of the test suite detects maximum number of defects for the given budget. Test prioritization becomes even more important when the test execution is time consuming, e.g., manual system tests vs. automated unit tests. Most existing test case prioritization techniques are based on code coverage, which requires access to source code. However, manual testing is mainly done in a black- box manner (manual testers do not have access to the source code). Therefore, in this paper, we first examine the existing test case prioritization techniques and modify them to be applicable on manual black-box system testing. We specifically study a coverage- based, a diversity-based, and a risk driven approach for test case prioritization. Our empirical study on four older releases of Mozilla Firefox shows that none of the techniques are strongly dominating the others in all releases. However, when we study nine more recent releases of Firefox, where the development has been moved from a traditional to a more agile and rapid release environment, we see a very signifiant difference (on average 65% effectiveness improvement) between the risk-driven approach and its alternatives. Our conclusion, based on one case study of 13 releases of an industrial system, is that test suites in rapid release environments, potentially, can be very effectively prioritized for execution, based on their historical riskiness; whereas the same conclusions do not hold in the traditional software development environments.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".