Machine learning (ML) techniques increase the effectiveness of software engineering (SE)
lifecycle activities. We systematically collected, quality-assessed, summarized, and …

In the past decade, brain and autonomic nervous system activity measurement received
increasing attention in the study of software engineering (SE). This paper presents a …

Trust is a crucial factor for the adoption and responsible usage of generative AI tools in
complex tasks such as software engineering. However, we have a limited understanding of …

The field of plant disease diagnosis and epidemiology seeks to assess symptoms caused by
pathogens. Different infectious and non-infectious agents can cause similar symptoms in …

While revolutionary AI-powered code generation tools have been rising rapidly, we know
little about how and how to help software developers form appropriate trust in those AI tools …

The field of software‐development effort estimation explores ways of defining effort through
prediction approaches. Even though this field has a crucial impact on budgeting and project …

Context: Cognitive load in software engineering refers to the mental effort users spend while
reading software artifacts. The cognitive load can vary according to tasks and across …

With the increasing influence of machine learning algorithms in decision-making processes,
concerns about fairness have gained significant attention. This area now offers significant …

Cognition plays a fundamental role in most software engineering activities. This article
provides a taxonomy of cognitive concepts and a survey of the literature since the beginning …

The comprehension of source code is a task inherent to many software development
activities. Code change, code review and debugging are examples of these activities that …