What do programmers know about the energy consumption of software?
Bibliographic record
Abstract
Traditionally, programmers have received a wide range of training on programming languages and methodologies, but rarely about software energy consumption. Yet, the popularity of mobile devices and cloud computing require increased awareness about software energy consumption. On a mobile device, computation is often limited by the battery life. Under the demands of cloud computing, data centers struggle to reduce energy consumption through vir- tualization and data center infrastructure management (DCIM) systems. Efficient energy consumption of software is increasingly becoming an important non-functional requirement for programmers. However, are programmers knowledgeable enough about software energy consumption? Do programmers base their implementation decision on popular beliefs? In this article, we survey over 100 programmers for their knowledge of software energy con- sumption. We find that programmers have limited knowledge about energy efficiency, lack the knowledge about the best practice to reduce energy consumption of software, and are often unsure about how software consumes energy. Education about the importance of energy effective software will benefit the programmers. Our results highlight the need for training about energy consumption and efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".