Abstract The rapid adoption of Artificial Intelligence (AI) and Generative AI (GenAI) tools is transforming the creation, maintenance, and dissemination of research software. Despite their growing prevalence, the implications of these technologies for Research Software Engineering (RSE) practices remain underexplored. This work introduces AI4RSE , an emerging research domain focused on the integration of AI into the development lifecycle of research software. To investigate current trends in AI-augmented RSE, we conducted an empirical study of more than 1,500 open-source research software repositories hosted on Zenodo. Each repository was assessed using a quadrant-based typology defined by two key dimensions: software engineering maturity and the level of AI integration. Our analysis combined static and semantic code inspection, evaluation of alignment with the FAIR Principles for Research Software (FAIR4RS), and heuristic classification of generative AI usage and MLOps adoption. Repositories are categorized into four development modes: Exploratory Coding , Vibe Coding , RSE , and AI4RSE , which reflect different levels of process rigor and AI tool integration. While many projects exhibit informal development patterns, a growing subset demonstrates mature, AI-assisted workflows. This landscape reveals key challenges, such as reproducibility risks and licensing ambiguity, while also highlighting emerging opportunities, including AI-assisted testing and intelligent documentation generation. The findings support a research agenda for AI4RSE, outlining benchmarks, guidelines, and community standards to promote responsible, reproducible, and scalable adoption of AI in scientific software development.

Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results" often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of $\color{green}{\text{Green AI}}$ to consider energy consumption as a metric of equal importance to accuracy and to reduce any irrelevant tasks or energy usage. We examine the training stage of the deep learning pipeline from a sustainability perspective, through the study of hyperparameter tuning strategies and the model complexity, two factors vastly impacting the overall pipeline’s energy consumption. First, we investigate the effectiveness of grid search, random search and Bayesian optimisation during hyperparameter tuning, and we find that Bayesian optimisation significantly dominates the other strategies. Furthermore, we analyse the architecture of convolutional neural networks with the energy consumption of three prominent layer types: convolutional, linear and ReLU layers. The results show that convolutional layers are the most computationally expensive by a strong margin. Additionally, we observe diminishing returns in accuracy for more energy-hungry models. The overall energy consumption of training can be halved by reducing the network complexity. In conclusion, we highlight innovative and promising energy-efficient practices for training deep learning models. To expand the application of $\color{green}{\text{Green AI}}$, we advocate for a shift in the design of deep learning models, by considering the trade-off between energy efficiency and accuracy.

Artificial intelligence is bringing ever new functionalities to the realm of mobile devices that are now considered essential (e.g., camera and voice assistants, recommender systems). Yet, operating artificial intelligence takes up a substantial amount of energy. However, artificial intelligence is also being used to enable more energy-efficient solutions for mobile systems. Hence, artificial intelligence has two faces in that regard, it is both a key enabler of desired (efficient) mobile functionalities and a major power draw on these devices, playing a part in both the solution and the problem. In this paper, we present a review of the literature of the past decade on the usage of artificial intelligence within the realm of green mobile computing. From the analysis of 34 papers, we highlight the emerging patterns and map the field into 13 main topics that are summarized in details.Our results showcase that the field is slowly increasing in the past years, more specifically, since 2019. Regarding the double impact AI has on the mobile energy consumption, the energy consumption of AI-based mobile systems is under-studied in comparison to the usage of AI for energy-efficient mobile computing, and we argue for more exploratory studies in that direction. We observe that although most studies are framed as solution papers (94%), the large majority do not make those solutions publicly available to the community. Moreover, we also show that most contributions are purely academic (28 out of 34 papers) and that we need to promote the involvement of the mobile software industry in this field.

The batch size is an essential parameter to tune during the development of new neural networks. Amongst other quality indicators, it has a large degree of influence on the model’s accuracy, generalisability, training times and parallelisability. This fact is generally known and commonly studied. However, during the application phase of a deep learning model, when the model is utilised by an end-user for inference, we find that there is a disregard for the potential benefits of introducing a batch size. In this study, we examine the effect of input batching on the energy consumption and response times of five fully-trained neural networks for computer vision that were considered state-of-the-art at the time of their publication. The results suggest that batching has a significant effect on both of these metrics. Furthermore, we present a timeline of the energy efficiency and accuracy of neural networks over the past decade. We find that in general, energy consumption rises at a much steeper pace than accuracy and question the necessity of this evolution. Additionally, we highlight one particular network, ShuffleNetV2 (2018), that achieved a competitive performance for its time while maintaining a much lower energy consumption. Nevertheless, we highlight that the results are model dependent.