GreenAdvisor: A tool for analyzing the impact of software evolution on energy consumption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Change-impact analysis, namely “identifying the potential consequences of a change” is an important and well studied problem in software evolution. Any change may potentially affect an application's behaviour, performance, and energy consumption profile. Our previous work demonstrated that changes to the system-call profile of an application correlated with changes to the application's energy-consumption profile. This paper evaluates and describes GreenAdvisor, a first of its kind tool that systematically records and analyzes an application's system calls to predict whether the energy-consumption profile of an application has changed. The GreenAdvisor tool was distributed to numerous software teams, whose members were surveyed about their experience using GreenAdvisor while developing Android applications to examine the energy-consumption impact of selected commits from the teams' projects. GreenAdvisor was evaluated against commits of these teams' projects. The two studies confirm the usefulness of our tool in assisting developers analyze and understand the energy-consumption profile changes of a new version. Based on our study findings, we constructed an improved prediction model to forecast the direction of the change, when a change in the energy-consumption profile is anticipated. This work can potentially be extremely useful to developers who currently have no similar tools.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it