Rafael Capilla

Marco Aiello, Mina Alipour, Antonio Brogi et al.

The sustainability impacts of ICT systems are difficult to assess and govern due to structural complexity, fragmented measurement practices, and unclear responsibilities across system layers. We argue that these challenges cannot be addressed solely by metrics and motivate the need for a shared Green ICT reference framework that integrates sustainability across multiple perspectives and domains, lifecycle phases, and governance contexts. We present an initial framework developed within the Informatics Europe Green ICT Working Group as a first step towards a comprehensive reference framework.

Rafael Capilla, J. Andrés Díaz-Pace, Yamid Ramírez et al.

Architecture evaluation methods have long been used to evaluate software designs. Several evaluation methods have been proposed and used to analyze tradeoffs between different quality attributes. Having competing qualities leads to conflicts for selecting which quality-attribute scenarios are the most suitable ones that an architecture should tackle and for prioritizing the scenarios required by the stakeholders. In this context, architecture evaluation is carried out manually, often involving long brainstorming sessions to decide which are the most adequate quality scenarios. To reduce this effort and make the assessment and selection of scenarios more efficient, we suggest the usage of LLMs to partially automate evaluation activities. As a first step to validate this hypothesis, this work studies MS Copilot as an LLM tool to analyze quality scenarios suggested by students in a software architecture course and compares the students' results with the assessment provided by the LLM. Our initial study reveals that the LLM produces in most cases better and more accurate results regarding the risks, sensitivity points and tradeoff analysis of the quality scenarios. Overall, the use of generative AI has the potential to partially automate and support the architecture evaluation tasks, improving the human decision-making process.

Karthik Shivashankar, Rafael Capilla, Maren Maritsdatter Kruke et al.

A systems quality is a major concern for development teams when it evolve. Understanding the effects of a loss of quality in the codebase is crucial to avoid side effects like the appearance of technical debt. Although the identification of these qualities in software requirements described in natural language has been investigated, most of the results are often not applicable in practice, and rely on having been validated on small datasets and limited amount of projects. For many years, machine learning (ML) techniques have been proved as a valid technique to identify and tag terms described in natural language. In order to advance previous works, in this research we use cutting edge models like Transformers, together with a vast dataset mined and curated from GitHub, to identify what text is usually associated with different quality properties. We also study the distribution of such qualities in issue trackers from openly accessible software repositories, and we evaluate our approach both with students from a software engineering course and with its application to recognize security labels in industry.