arXiv subjects

Fabiano Dalpiaz

Publications and source records attributed to Fabiano Dalpiaz.

8 recordsLinked to original sources

arXiv2026

A Systematic Analysis of Higher Education on Software Engineering in the Netherlands

Objectives. Software engineering educators strive to continuously improve and refine their courses and programs. Understanding the current state of practice of software engineering higher education can empower educators to critically assess their courses, fine-tune them, and ultimately enhance their educational curricula. In this study, we provide an encompassing analysis of higher education on software engineering by considering the educational offering of the Netherlands. Study methods. We adopt a crowdsourced analysis considering 10 Dutch universities and 207 courses. Courses are analysed via a set of key knowledge areas adapted from the SWEBOK, which are mapped to courses by educators of their universities. The mapping process is refined via homogenisation and internal consistency improvement phases, followed by a data analysis phase. Findings. Given its fundamental nature, Construction and Programming is the most covered knowledge area at Bachelor level. Other knowledge areas are equally covered at Bachelor and Master level (e.g., software engineering models), while more advanced ones are almost exclusively provided at Master level (e.g., Maintenance). Three clusters of tightly coupled knowledge areas emerge: (i) requirements, architecture, and design, (ii) testing, verification, and security, and (iii) process-oriented and DevOps topics. Dutch universities cover all knowledge areas uniformly, with minor deviations reflecting institutional research strengths. Conclusions. Our results highlight correlations among key software engineering knowledge areas. We also identify underrepresented areas, such as software economics, which educators may consider including in curricula. We invite researchers to make use of our research method in their own geographical region to globally compare software engineering education programs.

cs.SE↗

arXiv2026

Automated Alignment between Elicitation Interviews and Requirements

Software requirements are derived from a variety of elicitation techniques, many of which have a conversational nature, like interviews. However, evaluating whether those derived requirements faithfully reflect the stakeholders' needs remains a challenging manual task. In this paper, we formalize the task of aligning the transcript of an interview with a collection of requirements represented as user stories. We propose two heuristic metrics for alignment, called (i) requirements faithfulness: the proportion of stories supported by the transcript, and (ii) interview coverage: the proportion of transcript supported by at least one story. Then, we run experiments with large language models and embedding models that assess the ability of evaluating these metrics automatically. Experiments over four datasets show that an LLM-based solution achieves 0.86 macro-F1 on manually labeled chunk-story pairs. We also show how embedding models can be used as blockers to make the approach more scalable. This work paves the way for more research on linking conversational artifacts with requirements. The formal framework and the automated matching techniques are basic components that can be used for emerging tasks such as tracing requirements to interviews and generating requirements from conversations.

cs.CL↗

arXiv2025

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs

Software qualities such as usability or reliability are among the strongest determinants of mobile app user satisfaction and constitute a significant portion of online user feedback on software products, making it a valuable source of quality-related feedback to guide the development process. The abundance of online user feedback warrants the automated identification of quality characteristics, but the online user feedback's heterogeneity and the lack of appropriate training corpora limit the applicability of supervised machine learning. We therefore investigate the viability of three approaches that could be effective in low-data settings: language patterns (LPs) based on quality-related keywords, instructions for crowdsourced micro-tasks, and large language model (LLM) prompts. We determined the feasibility of each approach and then compared their accuracy. For the complex multiclass classification of quality characteristics, the LP-based approach achieved a varied precision (0.38-0.92) depending on the quality characteristic, and low recall; crowdsourcing achieved the best average accuracy in two consecutive phases (0.63, 0.72), which could be matched by the best-performing LLM condition (0.66) and a prediction based on the LLMs' majority vote (0.68). Our findings show that in this low-data setting, the two approaches that use crowdsourcing or LLMs instead of involving experts achieve accurate classifications, while the LP-based approach has only limited potential. The promise of crowdsourcing and LLMs in this context might even extend to building training corpora.

cs.SE↗

arXiv2024

Replication in Requirements Engineering: the NLP for RE Case

[Context]} Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks' inherent hairiness, and, in turn, the heterogeneous reporting structure. [Solution] To address these issues, we propose a new artifact, referred to as ID-Card, whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. [Results] In this paper: (i) we report on hands-on experiences of replication, (ii) we review the state-of-the-art and extract replication-relevant information, (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction, and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. [Contribution] This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication, but can also be used in other contexts, e.g., for educational purposes.

cs.SE↗

arXiv2022

A Semi-automated Method for Domain-Specific Ontology Creation from Medical Guidelines

The automated capturing and summarization of medical consultations has the potential to reduce the administrative burden in healthcare. Consultations are structured conversations that broadly follow a guideline with a systematic examination of predefined observations and symptoms to diagnose and treat well-defined medical conditions. A key component in automated conversation summarization is the matching of the knowledge graph of the consultation transcript with a medical domain ontology for the interpretation of the consultation conversation. Existing general medical ontologies such as SNOMED CT provide a taxonomic view on the terminology, but they do not capture the essence of the guidelines that define consultations. As part of our research on medical conversation summarization, this paper puts forward a semi-automated method for generating an ontological representation of a medical guideline. The method, which takes as input the well-known SNOMED CT nomenclature and a medical guideline, maps the guidelines to a so-called Medical Guideline Ontology (MGO), a machine-processable version of the guideline that can be used for interpreting the conversation during a consultation. We illustrate our approach by discussing the creation of an MGO of the medical condition of ear canal inflammation (Otitis Externa) given the corresponding guideline from a Dutch medical authority.

cs.SE↗

arXiv2021

The Complexity of Data-Driven Norm Synthesis and Revision

Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should follow in order to achieve the objective of the MAS. However, designing norms to achieve a particular system objective can be difficult, particularly when there is no direct link between the language in which the system objective is stated and the language in which the norms can be expressed. In this paper, we consider the problem of synthesising a norm from traces of agent behaviour, where each trace is labelled with whether the behaviour satisfies the system objective. We show that the norm synthesis problem is NP-complete.

cs.CC↗

arXiv2018

Reasoning about Norms Revision

Norms with sanctions have been widely employed as a mechanism for controlling and coordinating the behavior of agents without limiting their autonomy. The norms enforced in a multi-agent system can be revised in order to increase the likelihood that desirable system properties are fulfilled or that system performance is sufficiently high. In this paper, we provide a preliminary analysis of some types of norm revision: relaxation and strengthening. Furthermore, with the help of some illustrative scenarios, we show the usefulness of norm revision for better satisfying the overall system objectives.

cs.MA↗

arXiv2016

iStar 2.0 Language Guide

The i* modeling language was introduced to fill the gap in the spectrum of conceptual modeling languages, focusing on the intentional (why?), social (who?), and strategic (how? how else?) dimensions. i* has been applied in many areas, e.g., healthcare, security analysis, eCommerce. Although i* has seen much academic application, the diversity of extensions and variations can make it difficult for novices to learn and use it in a consistent way. This document introduces the iStar 2.0 core language, evolving the basic concepts of i* into a consistent and clear set of core concepts, upon which to build future work and to base goal-oriented teaching materials. This document was built from a set of discussions and input from various members of the i* community. It is our intention to revisit, update and expand the document after collecting examples and concrete experiences with iStar 2.0.

cs.SE↗