Improving Test Effectiveness Using Test Executions History: An Industrial Experience Report
Bibliographic record
Abstract
The cost of software testing has become a burden for software companies in the era of rapid release and continuous integration. Our industrial collaborator Ericsson also faces the challenges of expensive testing processes which are typically part of a complex and specialized testing environment. In order to assist Ericsson with improving the test effectiveness of one of its large subsystems, we adopt test selection and prioritization approaches based on test execution history from prior research. By adopting and simulating those approaches on six months of testing data from our subject system, we confirm the existence of valuable information in the test execution history. In particular, the association between test failures provide the most value to the test selection and prioritization processes. More importantly, during this exercise, we encountered various challenges that are unseen or undiscussed in prior research. We document the challenges, our solutions and the lessons learned as an experience report. Our experiences can be valuable for other software testing practitioners and researchers who would like to adopt existing test effectiveness improvement approaches into their work environment.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".