# Studying the Impact of User Feedback on Software Energy Consumption

Thomas Zaragoza, Adel Noureddine, Ernesto Exposito

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Thomas Zaragoza, Adel Noureddine, Ernesto Exposito. Studying the Impact of User Feedback on Software Energy Consumption. The 22nd International Conference on Pervasive Computing and Communications (PerCom 2024), Mar 2024, Biarritz, France. hal-04395267

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# Studying the Impact of User Feedback on Software Energy Consumption

Thomas Zaragoza*, Adel Noureddine†, and Ernesto Exposito‡

Université de Pau et des Pays de l'Adour, E2S UPPA, LIUPPA, Pau, France

*thomas.zaragoza@univ-pau.fr, †adel.noureddine@univ-pau.fr, ‡ernesto.exposito@univ-pau.fr

***Abstract***—With the rise in greenhouse gas emissions (GHGE), the need for sustainable solutions amid the environmental crisis is growing. Information and communication technologies (ICT) reduce emissions in other sectors, but they also have a rising carbon footprint and increased energy consumption. Our societies today rely on these technologies, and human needs and interactions are the main driving force behind their widespread use. Therefore, it is crucial to address the human role in energy consumption and carbon emission of ICT. Our work aims at raising end user awareness about ICT, and in particular about software, with an objective of reducing software energy through user behavior change. Motivated by a lack of user-centric approaches in energy feedback systems, we propose rigorous field experiments and surveys using persuasive technologies to optimize software energy consumption in ICT. We propose to first categorize feedback characteristics and audiences through a taxonomy. Then, we aim to study feedback impact on user awareness and software energy reduction through quantitative and qualitative field studies.

***Index Terms***—Energy consumption; Software Engineering; Feedback; Field Studies; Behavioral Studies

## I. INTRODUCTION

The current state of our planet is marked by an unprecedented environmental crisis, characterized by rising greenhouse gas emissions (GHGE), contributing significantly to global warming. Information and communication technology (ICT) act as a catalyst and a recipient for sustainability efforts, aiding in decarbonizing various sectors despite contributing to its own steadily increasing carbon footprint. In [1], Charlotte Freitag et al. conducted a detailed analysis and assessment on the estimates, trends and regulations regarding the impact of ICT and roughly estimate its GHGE to be ca. 2.1%–3.9% of 2020’s global GHGE which can be further decomposed to ca. 30% originating from embodied emissions (production, transportation, use, and disposal) and ca. 70% coming from use phase emissions (emissions associated with the operation and utilization of a product or service throughout its intended lifespan: in this case mostly being energy-related use phase emissions through energy consumption). Key approaches to reducing energy consumption in ICT are to design energy-efficient hardware architectures, improve cooling systems, optimize software, data center efficiency and cloud computing / virtualization. We believe that while these approaches have their merits, we also believe they have the long-term flaw of being unsustainable without intervening on the human element of energy consumption. The rebound effect, intricately linked to Jevons’ Paradox, describes the situation where improvements in resource efficiency or technology, paradoxically

results in the overall increase of consumption or demand. We can trace this phenomenon’s origins back to William Stanley Jevons, a 19th-century economist, who observed that improvements in coal-powered technologies didn’t decrease coal consumption as anticipated but instead increased it due to expanded usage [2]. Transitioning from the foundational understanding of energy consumption dynamics and the impact of human behaviour and with the aim of raising awareness in software energy consumption, in our work-in-progress approach we will first commence with the systematic classification of environmentally conscious feedback mechanisms, progressing to the nuanced profiling of users based on various criteria, and culminating in the development and empirical validation of tailored feedback tools through exhaustive experimentation. By experimenting on the individual effects of various feedback components on user awareness and software energy consumption according to user profiles and workloads we expect to produce a solid knowledge base in which we can identify which feedback is the most appropriate according to the context.

We aim to address the following questions :

* RQ1: Can green feedback drive behavioral change in software usage?

* RQ2: Can behavioral change significantly reduce users’ software energy consumption?

These research questions aim to explore the interconnected dynamics of environmental and ecological consciousness, individual behaviors, computing and software sustainability. Firstly, RQ1 delves into the potential impacts of green feedback on driving behavioral change. In particular, we investigate whether information emphasizing environmentally friendly practices can influence individuals to adopt more computing sustainable behaviors. By using pervasive technologies, monitoring software energy consumption, and user activity we aim to directly intervene on user habits through feedback by encouraging smarter software usage (Green alternatives, setting optimizations, reduced idle use, etc.). RQ2 examines the effectiveness of behavioral changes in mitigating user software energy consumption, seeking to quantify the extent to which altered behaviors can lead to a meaningful reduction in energy use. In case where software usage behavioral change does not lead to significant reduced software energy consumption, there would still be an interest in studying the potential a *spill over* effect into different facets of their life. Existing works
in eco-feedback and green awareness often target buildings and smart homes where the focus is on reducing the energy consumption of non-computing devices. A recent study [3] showed that most approaches influence occupants' behavior in buildings are grouped into three categories: eco-feedback, social interactions, and gamification. Eco-feedback approaches focus on providing users with a feedback of their energy consumption or usage, can use metaphors (such as trees [4]), and can lead to important savings [5]. Social interactions aim to compare the energy behavior and savings between users in a common household, building or office. While gamification integrates users into social or interactive games. For instance, in [6], the authors proposed a virtual pet game, *Energy Chickens*, to help reduce energy consumption in a commercial office by representing office worker's energy consumption through the evolution of their in-game pet and rewarding good behavior with in-game currency. In [7], *EnergyWiz* is proposed and allows users to compare their energy consumption over time, on average and with a peer contact from a social platform. However, the energy consumption of software and how users interact with, is rarely taken into consideration in existing studies. When software is the main concern of feedback approaches, the audience is usually developers and practitioners, rather than end users. For instance, in [8], the authors proposed the *Bugu* service to analyze power and event information of mobile applications to identify potential energy optimization areas such as radio service and hardware interrupts. Similarly, in [9], a tool is proposed to aid developers in optimizing Android applications by analyzing their energy consumption at a method level and identifying areas of energy expenditure, utilizing user-defined test cases, code injection methods, and bytecode analysis. The study most closely related to our research involved observing 100 students in a field study, examining their behavior when given real-time feedback on their computer's energy consumption in [10]. The study analyses one visual feedback (a window popup with numbers indicating power consumption, electricity price, and CO2 emission, and a graph of power consumption). Our aim is to push further with studying various feedback types and forms on various user profiles. We present our work progress on feedback taxonomy and ontology in Section II. We outline the experimental protocol of our field studies in Section III. Finally, we conclude and present the future works in Section IV.

## II. FEEDBACK TAXONOMY

In this section, we present a taxonomy for computing and software eco-feedback, categorizing approaches based on their utility in offering green information and their impact on user profiles. Eco-feedback aims to raise awareness, persuade, and facilitate user intervention for energy consumption reduction. Feedback characteristics include target audience, data structure, presentation format, and additional elements to enhance persuasive, intervention, or insightful capabilities.

### A. Target audience

Feedback system characteristics are influenced by the target audience, dictating its persuasive, intervening and insightful needs. Three user categories are identified: 1) experts necessitate high-accuracy and detailed data, 2) end-users seek persuasive and simplified feedback, and 3) decision-makers require big picture understanding of the data collected. This distinction guides the design, autonomous or human-dependent, of feedback systems for environmental interventions.

### B. Methods

Feedback methods are the narrative by which data will be structured. From our findings, we have been able to distinct the following 7 methods :

* Ambient feedback alters the physical environment using visual, auditory, or sensory cues. For instance, a smart plug's LED colors indicating energy consumption or scents diffused during peak or off-peak hours in a smart grid setup.

* Historical (self-comparative) analysis. For instance, a user could gauge their software energy consumption rates between months to determine if they're improving.

* Peer comparisons fosters social interaction. For instance, a user could compare their software energy usage with friends or family, which both provides an additional reference point and fosters a sense of community and or competition.

* Normative feedback compares to the average of similar profiles. For instance, a user could compare their software energy usage with friends or family, which both provides an additional reference point and fosters a sense of community and or competition.

* Desegregated feedback offers detailed breakdowns for fine-grained analysis. For instance, a user could break down their total system energy consumption by software, allowing for insights on areas to improve on.

* Injunctive feedback guides behaviors aligning with norms. For instance, through simple visual queues we can signal to the user to change their behavior. Green means go and red means stop.

* Plain feedback presents real-time data as a whole with little to no additional analysis by the feedback system.

### C. Data representation

Data visualization plays a pivotal role in conveying complex findings and results in a concise, accessible, and impacting manner. The target audience, feedback method and context all impact the effectiveness of a data visualization form and consequently the effectiveness of feedback as a whole. For this reason, in this paper, we'll be looking at three individual components of data visualization: graphs, metrics and metaphors.

**Graphs:** While there is a long exhaustive list of conventional data visualisation methods, and a near infinite of possibilities and customization, we have listed the following as the most recurring: Numerical display, line, pie or column charts.
**Metrics:** Choosing suitable metrics in data visualization significantly influences information comprehension and decision-making, especially when humans are the target audience of a feedback system. Metrics establish the basis for scrutinizing data aspects, influencing the narrative they convey and ensuring relevance to the objectives. We found Joules(J) and Watt-hours (Wh) to be the most recurring.

**Metaphors:** Metaphors simplify complexity visually, aiding in understanding and retention of information, shaping the audience's perception and facilitating interpretation within data visualization. We haven't encountered the usage of metaphors often but could be for example: representing software energy consumption in kilometers a car could drive using the same energy.

### D. Augments

When designing a feedback system, we can implement various additional features which can significantly enhance the effectiveness of feedback systems. These miscellaneous features, include (but not limited to) artificial intelligence, goal setting, actuators, notifications, gamification, social interactions, and incentives. Incorporating these features enriches feedback systems, enhancing their persuasive, intervening and insightful capabilities.

**Persuasive:** Goal setting defines specific objectives, motivating action and fostering a sense of accomplishment upon achievement. Gamification engages users through game elements, social interactions foster collaboration and understanding, while incentives encourage active participation and improvement within feedback exchanges.

**Intervention:** Artificial intelligence leverages contextual data and user behavior to offer tailored recommendations and scheduling to help users plan interventions, while actuators serve in executing interventions, particularly in autonomous systems. Notifications deliver concise updates across various mediums, disrupting habits, and signaling when interventions need to take place.

**Insights:** Artificial intelligence leverages contextual data and user behavior to forecast consumption if behavior is unaltered, providing further insights.

## III. EXPERIMENTAL PROTOCOL

The aim of our field studies is to analyze the impact and effectiveness of various feedback approaches on users' behaviors regarding software usage and energy consumption. Our experiment considers the potential impact of user profiles and workloads as extraneous variables.

To answer our research questions RQ1 and RQ2, in our field experiments we'll be studying the effect of feedback on software energy consumption and user behavior while cautiously taking into consideration the effect of user profiles and workloads. In the following sections, respectively dependent and extraneous variables, we'll go more into the details before concluding on our expected results.

### A. Dependent variables

In a scientific study, dependent variables are the outcomes that are observed, measured, or tested to assess the effects or influence of independent variables. In this context, we're studying the effects the composition of feedback has on software energy consumption and user behavior. **Software energy consumption** can currently be *soft monitored* through measuring the use rate of individual hardware components per software process and used to calculate energy consumption through estimation models. The complexity of which, when distributed to end-users, lies in being flexible enough to adapt to different hardware components with different specifications while being accurate enough to detect shifts in trends of energy consumption. Additionally, it has to be user-friendly enough to be installed by the average end-user.

Understanding **user behaviors** represents a multifaceted challenge, blending both qualitative and quantitative dimensions. By employing surveys, we aim to gauge shifts in user awareness regarding software energy consumption, discerning whether provided feedback has effectively informed them about the impact of their behavior on software energy usage. Moreover, our investigation seeks to explore whether exposure to green feedback regarding software energy consumption extends beyond software interfaces, influencing other aspects of their lives positively or negatively. To establish connections between potential behavior modifications, software energy reductions, and the green feedback system, it is crucial to analyze user interaction with the system. This analysis involves survey-based inquiries and qualitative assessments, tracking user activities alongside feedback data. Detecting active user windows and interactions, such as clicks (e.g., altering feedback, refreshing, opening/minimizing), offers valuable insights into user engagement with the system.

### B. Extraneous variables

Extraneous variables, also known as confounding variables, are those that aren't the primary focus of a study but can impact the relationship between independent and dependent variables, potentially introducing bias or misleading associations into the study results. In our case, socio-demographic status, environmental views, and technical proficiency are considered key extraneous variables influencing our experiment's outcomes. For instance, differences in these factors among candidate groups might skew the results, hence we aim to ensure homogeneity across groups to mitigate statistical differences. However, our goal is to encompass a diverse candidate pool to create an inclusive feedback knowledge base tailored to individual profiles. To discern user profiles' connection with feedback efficiency, we plan to utilize classification schemes from organizations like UNESCO or the French's INSEE for socio-demographic profiling, integrating additional inquiries on gender, age, household income, and resource management roles within the household. To gauge users' environmental perspectives, we will employ the revised New Environmental Paradigm (NEP), a commonly used questionnaire assessing individuals' environmental attitudes [11]. Additionally, we will
include tailored inquiries addressing the environmental impact of ICT. For evaluating candidates' technical proficiency, we will inquire about their familiarity with PCs and software usage habits, focusing on their typical workloads.

### C. Field test

We aim to conduct field test experiments to identify the most effective feedback method. Our plan involves recruiting a total of *n* participants, evenly distributing them among 8 distinct groups. These groups will be randomized, comprising 7 experimental groups (each using a different feedback method) and a control group.

Our focus is on analyzing the impact of feedback methods on software energy consumption and user awareness. To ensure accuracy, we'll refrain from introducing alterations and keep the data representation variables constant. Prior to the experiment, participants will undergo pre-experiment surveys. These surveys will gather information about their socio-demographic background, environmental consciousness, and awareness of software energy consumption.

The initial survey serves a dual purpose: first, to establish consistency among the experimental groups (minimizing the influence of external factors), and second, to uncover potential correlations between user profiles and software behavior. Participants will then participate in the experiment which will last less than a day. During this time, they will be asked to perform various tasks utilizing multiple software applications (such as navigation on web browsers or text editing using word processing, etc.) while receiving specific designated feedback. Throughout the experiment, we will discreetly monitor their software energy consumption and behavior. This includes tracking idle time, active window usage, software usage logs, and more.

Upon completion of the experiment, participants will be required to fill out another survey which will gauge their awareness of software energy consumption and gather feedback on their overall perception of the provided feedback. This qualitative data will provide valuable insights into design preferences and perceived usefulness and ease-of-use.

### D. Expected results

Our experiments will extensively and individually test every combination of independent variables (ranging from feedback methods to metaphors and to the other various augments) on a socially and environmentally diverse pool of candidates with various workload natures and hardware configuration. We aim to provide the groundwork for an initial knowledge base, from which, we will generate personalized feedback from users.

## IV. CONCLUSION

As ICT's energy consumption and emissions rise, it simultaneously serves as both a solution and contributor to sustainability challenges with existing energy reduction strategies often neglecting the human factor, leading to rebound effects. Our goal is to bridge this gap by developing a user-conscious feedback tool, aiming to raise awareness about

computing and software energy consumption using persuasive technologies. Driven by the absence of user-centric approaches in energy feedback systems, particularly in computing and in software, our focus is on optimizing software energy consumption. Through rigorous experiments and taxonomy classification, we aim to offer fresh insights into sustainable energy practices within ICT. Our protocol intends to reveal the impact of feedback variables on user awareness and software energy reduction, exploring the link between environmental consciousness, individual behavior, and ICT sustainability. The anticipated outcomes seek to establish a tailored feedback system, personalized to individual user profiles and contextual data.

## ACKNOWLEDGMENT

The research leading to this publication is supported by the French Research Agency through the ANR-22-CE25-0010 BEHAVE project.

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