Products go Green: Worst-Case Energy Consumption in Software Product Lines

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Couto, M (Author) Other; Borba, P (Author) Other; Cunha, J (Author) Other; Joao Paulo Fernandes (Author) Other; Pereira, R (Author) Other; Saraiva, J (Author) Other

Conference proceedings International

Title: 21ST INTERNATIONAL SYSTEMS & SOFTWARE PRODUCT LINE CONFERENCE (SPLC 2017), VOL 1

Pages: 84-93

21st International System and Software Product Line Conference (SPLC)

Sevilla, SPAIN, SEP 25-29, 2017

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 10 Citations

Other information

Authenticus ID: P-00M-Z9W

DOI: 10.1145/3106195.3106214

Abstract (EN): The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly. In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them. Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product. We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10