Bibliographic record
Abstract
Power consumption is increasingly becoming a concern for not only electrical engineers, but for software engineers as well, due to the increasing popularity of new power-limited contexts such as mobile-computing, smart-phones and cloud-computing. Software changes can alter software power consumption behaviour and can cause power performance regressions. By tracking software power consumption we can build models to provide suggestions to avoid power regressions. There is much research on software power consumption, but little focus on the relationship between software changes and power consumption. Most work measures the power consumption of a single software task; instead we seek to extend this work across the history (revisions) of a project. We develop a set of tests for a well established product and then run those tests across all versions of the product while recording the power usage of these tests. We provide and demonstrate a methodology that enables the analysis of power consumption performance for over 500 nightly builds of Firefox 3.6; we show that software change does induce changes in power consumption. This methodology and case study are a first step towards combining power measurement and mining software repositories research, thus enabling developers to avoid power regressions via power consumption awareness.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".