Catalog of Energy Patterns for Mobile Applications
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Abstract (arXiv)
Software engineers make use of design patterns for reasons that range from performance to code comprehensibility. Several design patterns capturing the body of knowledge of best practices have been proposed in the past, namely creational, structural and behavioral patterns. However, with the advent of mobile devices, it becomes a necessity a catalog of design patterns for energy efficiency. In this work, we inspect commits, issues and pull requests of 1027 Android and 756 iOS apps to identify common practices when improving energy efficiency. This analysis yielded a catalog, available online, with 22 design patterns related to improving the energy efficiency of mobile apps. We argue that this catalog might be of relevance to other domains such as Cyber-Physical Systems and Internet of Things. As a side contribution, an analysis of the differences between Android and iOS devices shows that the Android community is more energy-aware.
Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). First posted on arXiv 10 Jan 2019.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 0; 2025: 2.
8 papers.
- 2022Data-Centric Green AI: An Exploratory Empirical Studycited by 24
- 2023Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AIcited by 16
- 2021Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?cited by 6
- 2022Energy-Aware JPEG Image Compression: A Multi-Objective Approachcited by 2
- 2025Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizationscited by 1
- 2023On the Identification of the Energy related Issues from the App Reviewscited by 0
- 2023The Two Faces of AI in Green Mobile Computing: A Literature Reviewcited by 0
- 2025How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Studycited by 0
- 2025How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study31 Dec 2025
- 2025Can We Make Code Green? Understanding Trade-Offs in LLMs vs. Human Code Optimizations26 Mar 2025
- 2023The Two Faces of AI in Green Mobile Computing: A Literature Review21 Jul 2023
- 2023On the Identification of the Energy related Issues from the App Reviews22 Apr 2023
- 2023Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI24 Mar 2023
- 2022Energy-Aware JPEG Image Compression: A Multi-Objective Approach9 Sep 2022
- 2022Data-Centric Green AI: An Exploratory Empirical Study6 Apr 2022
- 2021Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?28 Sep 2021