A methodology for energy performance testing of smartphone applications
Bibliographic record
Abstract
Smartphones are becoming increasingly popular among users. They are equipped with an enormous number of applications, and these applications drain the smartphones' batteries. Moreover, battery capacity is significantly restricted due to constraints on size and weight of the device. It is important for smartphone applications to be energy efficient. Thus, a methodology to conduct energy performance testing is needed for two reasons: (i) evaluate the power consumption of a single application on a given device; (ii) compare the power consumption of different smartphones or platforms running the same application. In our earlier work “Selection and execution of user level test cases for energy cost evaluation of smartphones” (Proceedings of the 6th AST, 2011), we have developed a testing methodology that significantly reduces the number of test cases. In addition, we have introduced the concepts of primary and standalone test configurations. However, ordering of the executions of those two kinds of tests is non-trivial, and it was not studied in that paper. In this paper, we introduce a methodology to interleave the identification of those two kinds of test configurations in order to reduce the total number of configurations. We express the methodology in the form of a detailed flow chart that application developers can easily follow. We apply the methodology to a specific smartphone, namely HTC Nexus One smartphone in order to illustrate the process of this methodology. We have shown that the total number of test configurations obtained by the given methodology is the same as the number predicted by numerical expressions.
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 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".