In the past decade, research on measuring and assessing the environmental impact of software has gained significant momentum in science and industry. However, due to the large number of research groups, measurement setups, procedure models, tools, and general novelty of the research area, a comprehensive research framework has yet to be created. The literature documents several approaches from researchers and practitioners who have developed individual methods and models, along with more general ideas like the integration of software sustainability in the context of the UN Sustainable Development Goals, or science communication approaches to make the resource cost of software transparent to society. However, a reference measurement model for the energy and resource consumption of software is still missing. In this article, we jointly develop the Green Software Measurement Model (GSMM), in which we bring together the core ideas of the measurement models, setups, and methods of over 10 research groups in four countries who have done pioneering work in assessing the environmental impact of software. We briefly describe the different methods and models used by these research groups, derive the components of the GSMM from them, and then we discuss and evaluate the resulting reference model. By categorizing the existing measurement models and procedures and by providing guidelines for assimilating and tailoring existing methods, we expect this work to aid new researchers and practitioners who want to conduct measurements for their individual use cases.

For billions of deployed browsers, JavaScript provides the platform-independent lingua franca that enabled the triumphal march of web-based applications. Originally intended for simple UI-event processing, JavaScript comes with an event-driven programming model, where event-callback functions are executed in strict sequential order. However, with applications getting more complex and tasks becoming more computation intensive, its first-come-first-serve and run-to-completion semantic is hitting a limit, when reactions to user inputs are delayed beyond the human perception threshold. With the rise of the Internet of Things, this leads to friction-filled user experiences in everyday situations. With RT.js, we selectively introduce pseudo-preemption points into JavaScript functions and sequence the execution of event callbacks with well-known real-time scheduling policies, like EDF. Thereby, we provide a soft real-time abstraction that mitigates the described shortcomings of the JavaScript execution model without modifying the actual engine; making RT.js compatible with billions of devices. Applied to generated real-time task sets, we can almost eliminate the 30-percent deadline-miss ratios of baseline JavaScript at moderate costs. In a browser-based macro benchmark, we could diminish the influence of computation-intensive background tasks on the page-rendering performance.

Many authors have proposed criteria to assess the “environmental friendliness” or “sustainability” of software products. However, a causal model that links observable properties of a software product to conditions of it being green or (more general) sustainable is still missing. Such a causal model is necessary because software products are intangible goods and, as such, only have indirect effects on the physical world. In particular, software products are not subject to any wear and tear, they can be copied without great effort, and generate no waste or emissions when being disposed of. Viewed in isolation, software seems to be a perfectly sustainable type of product. In real life, however, software products with the same or similar functionality can differ substantially in the burden they place on natural resources, especially if the sequence of released versions and resulting hardware obsolescence is taken into account. In this article, we present a model describing the causal chains from software products to their impacts on natural resources, including energy sources, from a life-cycle perspective. We focus on (i) the demands of software for hardware capacities (local, remote, and in the connecting network) and the resulting hardware energy demand, (ii) the expectations of users regarding such demands and how these affect hardware operating life, and (iii) the autonomy of users in managing their software use with regard to resource efficiency. We propose a hierarchical set of criteria and indicators to assess these impacts. We demonstrate the application of this set of criteria, including the definition of standard usage scenarios for chosen categories of software products. We further discuss the practicability of this type of assessment, its acceptability for several stakeholders and potential consequences for the eco-labeling of software products and sustainable software design.