Related papers: Domain Knowledge in Requirements Engineering: A Sy…
Domain adaptation is a sub-field of machine learning that involves transferring knowledge from a source domain to perform the same task in the target domain. It is a typical challenge in machine learning that arises, e.g., when data is…
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets. However, purely data-driven models might struggle when very difficult…
Digitalization is forging its path in the architecture, construction, engineering, operation (AECO) industry. This trend demands not only solutions for data governance but also sophisticated cyber-physical systems with a high variety of…
One prerequisite for supervised machine learning is high quality labelled data. Acquiring such data is, particularly if expert knowledge is required, costly or even impossible if the task needs to be performed by a single expert. In this…
Specifying data requirements for machine learning (ML) software systems remains a challenge in requirements engineering (RE). This vision paper explores causal modelling as an RE activity that allows the systematic integration of prior…
Machine learning enables the extraction of useful information from large, diverse datasets. However, despite many successful applications, machine learning continues to suffer from performance and transparency issues. These challenges can…
Pre-Requirement Specification traceability is the activity of capturing relations between requirements and their sources, in particular user needs. Requirements are formal technical specifications in the solution space; needs are natural…
Natural Language Processing (NLP) is widely used to support the automation of different Requirements Engineering (RE) tasks. Most of the proposed approaches start with various NLP steps that analyze requirements statements, extract their…
Background/Context: Large Language Models (LLMs) demonstrate strong performance on low-dimensional software engineering optimization tasks ($\le$11 features) but consistently underperform on high-dimensional problems where Bayesian methods…
The prevalence of online content has led to the widespread adoption of recommendation systems (RSs), which serve diverse purposes such as news, advertisements, and e-commerce recommendations. Despite their significance, data scarcity issues…
The integration of knowledge extracted from diverse models, whether described by domain experts or generated by machine learning algorithms, has historically been challenged by the absence of a suitable framework for specifying and…
Software requirements are key elements that contribute to the quality and users satisfaction of the final system. In this work, Requirements Engineering (RE) of web sites is presented using an organizational semiotics perspective. They are…
Requirements Engineering (RE) requires the collaboration of various roles in SE, such as requirements engineers, stakeholders and other developers, and it is thus a highly human dependent process in software engineering (SE). Identifying…
Context: Responsibility gaps, long-recognized challenges in socio-technical systems where accountability becomes diffuse or ambiguous, have become increasingly pronounced in GenAI-enabled software. The generative and adaptive nature…
Requirements Engineering (RE) is one of the prime areas in software development. Since agile software development englobes several emerging techniques and advocates for continuous improvement, it urges the question of which agile RE…
The requirements elicited from stakeholders are typically informal, incomplete, ambiguous, and inconsistent. It is the task of Requirements Engineering to transform them into an eligible (formal, sufficiently complete, unambiguous,…
[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem…
The requirements elicited from stakeholders suffer from various afflictions, including informality, incompleteness, ambiguity, vagueness, inconsistencies, and more. It is the task of requirements engineering (RE) processes to derive from…
[Context] Engineering Artificial Intelligence (AI) software is a relatively new area with many challenges, unknowns, and limited proven best practices. Big companies such as Google, Microsoft, and Apple have provided a suite of recent…
In this position paper, we elaborate on the possibilities and needs to integrate Design Thinking into Requirements Engineering. We draw from our research and project experiences to compare what is understood as Design Thinking and…