Scheduling functional regression tests for IBM DB2 products
Bibliographic record
Abstract
Functional Regression Testing (FRT) is performed to ensure that a new version of a product functions properly as designed. In a corporate environment, the large numbers of test jobs and the complexity of scheduling the jobs on different platforms make performance of this testing an important issue. A grid provides an infrastructure for applications to use shared heterogeneous resources. Such an infrastructure may be used to solve large-scale testing problems or to improve application performance. FRT is a good candidate application for running on a grid because each test job can run separately, in parallel. However, experience indicates that such applications may suffer performance problems without a proper cost-based grid scheduling strategy.The Database Technology (DBT) Regression Test Team at IBM conducts the FRT for IBM® DB2® Universal DatabaseTM (DB2 UDB) products. As a case study, we examined the current test scheduling approach for the DB2 products. We found that the performance of the test scheduler suffers because it does not incorporate cost-dependent selection of jobs and slaves (testing IDs). Therefore, we have replaced the DB2 test scheduler with one that estimates jobs' run times, and then chooses slaves using those times. Although knowing a job's actual run time is difficult, we can use case-based reasoning to estimate it based on past experience. We create a case base to store historical data, and design an algorithm to estimate new jobs' run times by identifying cases that have executed in the past. The performance evaluation of our new scheduler shows a significant performance benefit over the original scheduler. In this paper, we also examine how machine specifications, such as the number of slaves running on a machine and the machine speed, affect application performance and run time estimation accuracy.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".