João Paulo Fernandes

Luís Cruz, João Paulo Fernandes, Maja H. Kirkeby et al.

The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The "Greening AI with Software Engineering" CECAM-Lorentz workshop (no. 1358, 2025) funded by the Centre Européen de Calcul Atomique et Moléculaire and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current results dedicated to advancing green software and AI research. The workshop was held February 3-7, 2025, in Lausanne, Switzerland. Through keynotes, flash talks, and collaborative discussions, participants identified and prioritized key challenges for the field. These included energy assessment and standardization, benchmarking practices, sustainability-aware architectures, runtime adaptation, empirical methodologies, and education. This report presents a research agenda emerging from the workshop, outlining open research directions and practical recommendations to guide the development of environmentally sustainable AI-enabled systems rooted in software engineering principles.

Filipe Santos, João Paulo Fernandes, Luís Macedo

Action selection based on flags is a Reinforcement Learning (RL) exploration policy that improves the exploration of the state space through the use of flags, which can identify the most promising actions to take in each state. The quantum counterpart of this exploration policy further improves upon this by taking advantage of a quadratic speedup for sampling flagged actions. This approach has already been successfully employed for the game of Checkers. In this work, we describe the application of this method to the context of Connect Four, in order to study its performance in a different setting, which can lead to a better generalization of the technique. We also kept track of a metric that wasn't taken into account in previous work: the average number of iterations to obtain a flagged action. Since going second is a significant disadvantage in Connect Four, we also had the intent of exploring how this more complex scenario would impact the performance of our approach. The experiments involved training and testing classical and quantum RL agents that played either going first or going second against a Randomized Negamax opponent. The results showed that both flagged exploration policies were clearly superior to a simple epsilon-greedy policy. Furthermore, the quantum agents did in fact sample flagged actions in less iterations. Despite obtaining tagged actions more consistently, the win rates between the classical and quantum versions of the approach were identical, which could be due to the simplicity of the training scenario chosen.

Cláudio Gomes, João Paulo Fernandes, Gabriel Falcao et al.

The rapid adoption of Electric Vehicles (EVs) poses challenges for electricity grids to accommodate or mitigate peak demand. Vehicle-to-Vehicle Charging (V2VC) has been recently adopted by popular EVs, posing new opportunities and challenges to the management and operation of EVs. We present a novel V2VC model that allows decision-makers to take V2VC into account when optimizing their EV operations. We show that optimizing V2VC is NP-Complete and find that even small problem instances are computationally challenging. We propose R-V2VC, a heuristic that takes advantage of the resulting totally unimodular constraint matrix to efficiently solve problems of realistic sizes. Our results demonstrate that R-V2VC presents a linear growth in the solution time as the problem size increases, while achieving solutions of optimal or near-optimal quality. R-V2VC can be used for real-world operations and to study what-if scenarios when evaluating the costs and benefits of V2VC.

Rui Abreu, Marco Couto, Luís Cruz et al.

This report describes the research goals and results of the Green Software Lab (GSL) research project. This was a project funded by Fundação para a Ciência e a Tecnologia (FCT) -- the Portuguese research foundation -- under reference POCI-01-0145-FEDER-016718, that ran from January 2016 till July 2020. This report includes the complete document reporting the results achieved during the project execution, which was submitted to FCT for evaluation on July 2020. It describes the goals of the project, and the different research tasks presenting the deliverables of each of them. It also presents the management and result dissemination work performed during the project's execution. The document includes also a self assessment of the achieved results, and a complete list of scientific publications describing the contributions of the project. Finally, this document includes the FCT evaluation report.

Wellington Oliveira, Hugo Matalonga, Gustavo Pinto et al.

In the last few years, a growing body of research has proposed methods, techniques, and tools to support developers in the construction of software that consumes less energy. These solutions leverage diverse approaches such as version history mining, analytical models, identifying energy-efficient color schemes, and optimizing the packaging of HTTP requests. In this chapter, we present a complementary approach. We advocate that developers should leverage software diversity to make software systems more energy-efficient. Our main insight is that non-specialists can build software that consumes less energy by alternating at development time between readily available, diversely-designed pieces of software implemented by third-parties. These pieces of software can vary in nature, granularity, and quality attributes. Examples include data structures and constructs for thread management and synchronization.

Rui Pereira, Marco Couto, Jácome Cunha et al.

This paper presents a detailed study of the energy consumption of the different Java Collection Framework (JFC) implementations. For each method of an implementation in this framework, we present its energy consumption when handling different amounts of data. Knowing the greenest methods for each implementation, we present an energy optimization approach for Java programs: based on calls to JFC methods in the source code of a program, we select the greenest implementation. Finally, we present preliminary results of optimizing a set of Java programs where we obtained 6.2% energy savings.

Jácome Cunha, João Paulo Fernandes, Rui Pereira et al.

One of the most important assets of any company is being able to easily access information on itself and on its business. In this line, it has been observed that this important information is often stored in one of the millions of spreadsheets created every year, due to simplicity in using and manipulating such an artifact. Unfortunately, in many cases it is quite difficult to retrieve the intended information from a spreadsheet: information is often stored in a huge unstructured matrix, with no care for readability or comprehensiveness. In an attempt to aid users in the task of extracting information from a spreadsheet, researchers have been working on models, languages and tools to query. In this paper we present an empirical study evaluating such proposals assessing their usage to query spreadsheets. We investigate the use of the Google Query Function, textual model-driven querying, and visual model-driven querying. To compare these different querying approaches we present an empirical study whose results show that the end-users' productivity increases when using model-driven queries, specially using its visual representation.