Proceedings of the third international workshop on Large scale testing
Bibliographic record
Abstract
It is our great pleasure to welcome you to the Fourth International Workshop on Large-Scale Testing (LT 2015), held in Austin, Texas, USA, on February 1st, 2015. Large-scale software systems must service thousands (e.g., enterprise applications) or even millions (e.g., e-commerce websites like Amazon) of concurrent users every day. Many field problems of these systems are due to their inability to scale to field workloads, rather than feature bugs. In addition to conventional functional testing (e.g., unit and integration testing), these systems must be tested with large volumes of concurrent requests (called the load) to ensure the quality of these systems. Large-scale testing includes all different objectives and strategies of testing large-scale software systems using load. Examples of large-scale testing include live upgrade testing, load testing, high availability testing, operational profile testing, performance testing, reliability testing, stability testing and stress testing. LT 2015 is a one-day workshop. The workshop participants consist of a mixture of academic and industrial researchers. A big emphasis of this workshop is to make the workshop interactive with many discussion slots assigned throughout the schedule. The workshop has two keynote talks: Load Testing Elasticity and Performance Isolation in Shared Execution Environments by Professor Samuel Kounev from University of Wurzburg and Challenges, Benefits and Best Practices of Performance Focused DevOps by Wolfgang Gottesheim from Compuware. In addition, the workshop also includes presentations from technical papers and industrial talks. Finally, there is a panel, which brings together industrial practitioners and academic researchers to discuss the opportunities and challenges associated with large-scale testing. We hope you enjoy the technical and social program. If you are not able to attend our workshop, we hope you will find the papers and talks in this workshop simulating. This workshop would not happen without the efforts of the program committee members who helped with timely and constructive reviews. In addition, we want to extend our gratitude to each author and presenter who submitted their work to the LT 2015 workshop.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.097 | 0.037 |
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".