Natural Language Processing for Requirements Engineering (NLP4RE) is an area of
research and development that seeks to apply natural language processing (NLP) …

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to
automate the cumbersome requirement handling process. This literature review summarizes …

Context: Requirements engineering (RE) researchers have been experimenting with
machine learning (ML) and deep learning (DL) approaches for a range of RE tasks, such as …

Ambiguity is a pervasive issue in natural-language requirements. A common source of
ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering …

This research introduces DesignQA, a novel benchmark aimed at evaluating the proficiency
of multimodal large language models (MLLMs) in comprehending and applying engineering …

Context: Classification of software requirements into different categories is a critically
important task in requirements engineering (RE). Develo** machine learning (ML) …

Natural language (NL) is arguably the most prevalent medium for expressing systems and
software requirements. Detecting incompleteness in NL requirements is a major challenge …

Acceptance testing is crucial to determine whether a system fulfills end-user requirements.
However, the creation of acceptance tests is a laborious task entailing two major …

Recommender systems for requirements are typically built on the assumption that similar
requirements can be used as proxies to retrieve similar software. When a stakeholder …

The increasing complexity of cyber-physical systems (CPSs) makes their design,
development and operation extremely challenging. Due to the nature of CPS that involves …