Recommending Energy-Efficient Java Collections

• Published in: 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR) pp. 160 - 170
• Main Authors: Oliveira, Wellington, Oliveira, Renato, Castor, Fernando, Fernandes, Benito, Pinto, Gustavo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2019
• Subjects: Benchmarks; Collections; Energy consumption; Java; Mobile; Optimization; Refactoring; Software systems; Static analysis; Synchronization
• ISSN: 2574-3864
• DOI: 10.1109/MSR.2019.00033

Over the last years, increasing attention has been given to creating energy-efficient software systems. However, developers still lack the knowledge and the tools to support them in that task. In this work, we explore our vision that energy consumption non-specialists can build software that consumes less energy by alternating, at development time, between third-party, readily available, diversely-designed pieces of software, without increasing the development complexity. To support our vision, we propose an approach for energy-aware development that combines the construction of application-independent energy profiles of Java collections and static analysis to produce an estimate of in which ways and how intensively a system employs these collections. By combining these two pieces of information, it is possible to produce energy-saving recommendations for alternative collection implementations to be used in different parts of the system. We implement this approach in a tool named CT+ that works with both desktop and mobile Java systems, and is capable of analyzing 40 different collection implementations of lists, maps, and sets. We applied CT+ to twelve software systems: two mobile-based, seven desktop-based, and three that can run in both environments. Our evaluation infrastructure involved a high-end server, a notebook, and three mobile devices. When applying the (mostly trivial) recommendations, we achieved up to 17.34% reduction in energy consumption just by replacing collection implementations. Even for a real world, mature, highly-optimized system such as Xalan, CT+ could achieve a 5.81% reduction in energy consumption. Our results indicate that some widely used collections, e.g., ArrayList, HashMap, and HashTable, are not energy-efficient and sometimes should be avoided when energy consumption is a major concern.