Instruction level power analysis and optimization of software
Journal of VLSI Signal Processing Systems - Special issue on technologies for wireless computing
Modeling Power Management for Hard Disks
MASCOTS '94 Proceedings of the Second International Workshop on Modeling, Analysis, and Simulation On Computer and Telecommunication Systems
Using Complete Machine Simulation for Software Power Estimation: The SoftWatt Approach
HPCA '02 Proceedings of the 8th International Symposium on High-Performance Computer Architecture
Energy-optimizing source code transformations for operating system-driven embedded software
ACM Transactions on Embedded Computing Systems (TECS)
Harnessing Green IT: Principles and Practices
IT Professional
Journal of Software Maintenance and Evolution: Research and Practice
Green tracker: a tool for estimating the energy consumption of software
CHI '10 Extended Abstracts on Human Factors in Computing Systems
WCRE '11 Proceedings of the 2011 18th Working Conference on Reverse Engineering
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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.