Related papers: Research in Global Software Engineering: A Systema…
Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a…
Software development projects management is a complex endeavor because it requires dealing with numerous unforeseen events that constantly arise along the way and that go against the expectations that had been established at the beginning.…
Robots are being applied in a vast range of fields, leading researchers and practitioners to write tasks more complex than in the past. The robot software complexity increases the difficulty of engineering the robot's software components…
In the international software engineering research community, the premier conference (ICSE) features since a decade a special track on the role of SE In Society (or SEIS track). In this work, we want to use the articles published in this…
Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices. Despite rapid adoption, there is limited empirical evidence on how GenAI is used in SE…
Context: Corporate training plays a strategic role in the continuous development of professionals in the software engineering industry. However, there is a lack of systematized understanding of how training initiatives are designed,…
Software architecture is the foundation of a system's ability to achieve various quality attributes, including software performance. However, there lacks comprehensive and in-depth understanding of why and how software architecture and…
Background: The need for empirical investigations in software engineering is growing. Many researchers nowadays, conduct and validate their solutions using empirical research. Survey is one empirical method which enables researchers to…
This document details the planning phase of a Systematic Mapping Study (SMS). Our goal is to identify the software patterns used during the RE phase, in real-world setting (i.e., in real projects), not in academia (toy projects) and, to…
Existing work on the practical impact of software engineering (SE) research examines industrial relevance rather than adoption of study results, hence the question of how results have been practically applied remains open. To answer this…
Context: Tertiary studies in software engineering (TS@SE) are widely used to synthesise evidence on a research topic systematically. As part of their protocol, TS@SE define inclusion and exclusion criteria (IC/EC) aimed at selecting those…
Researchers generally place the most trust in peer-reviewed, published information, such as journals and conference papers. By contrast, software engineering (SE) practitioners typically do not have the time, access or expertise to review…
Context: The Evidence-Based Software Engineering (EBSE) paradigm and the planning phase of a systematic literature review. Objective: A protocol to do a systematic literature review with detailed information about the processes suggested by…
[Background] The MVP concept has influenced the way in which development teams apply Software Engineering practices. However, the overall understanding of this influence of MVPs on SE practices is still poor. [Objective] Our goal is to…
Representative sampling appears rare in empirical software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field. This article therefore reports a…
[Context] Although software development is an inherently human activity, research in software engineering (SE) has long focused mostly on processes and tools, failing to recall about the human factors behind. Even when explored, researchers…
Background: Classifications in meta-research enable researchers to cope with an increasing body of scientific knowledge. They provide a framework for, e.g., distinguishing methods, reports, reproducibility, and evaluation in a knowledge…
Context: Empirical Software Engineering (ESE) faces increasing challenges due to data scale, methodological complexity, and reproducibility concerns. Large Language Models (LLMs) have emerged as promising tools to support empirical…
In the dynamic field of Software Engineering (SE), where practice is constantly evolving and adapting to new technologies, conducting research is a daunting quest. This poses a challenge for researchers: how to stay relevant and effective…
One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently…