Related papers: A Systematic Mapping Study and Practitioner Insigh…
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…
Software Process Improvement (SPI) encompasses the analysis and modification of the processes within software development, aimed at improving key areas that contribute to the organizations' goals. The task of evaluating whether the selected…
Gamification has been used to motivate and engage participants in software engineering education and practice activities. There is a significant demand for empirical studies for the understanding of the impacts and efficacy of gamification.…
Sustainability is an increasingly-studied topic in software engineering in general, and in software architecture in particular. There are already a number of secondary studies addressing sustainability in software engineering, but no such…
The relevance of code comprehension in a developer's daily work was recognized more than 40 years ago. Consequently, many experiments were conducted to find out how developers could be supported during code comprehension and which code…
Context: Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. With the…
Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML)…
Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects. While there is a large variety of available tools usable for…
Nowadays, intelligent systems and services are getting increasingly popular as they provide data-driven solutions to diverse real-world problems, thanks to recent breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML).…
Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this…
Although empirical research often underpins practical visualization guidelines, it remains unclear how well these research-driven insights are reflected in the guidelines practitioners actually use. In this paper, we investigate the…
Background: Previous research highlights that common misconceptions about developer productivity lead to harmful and inaccurate evaluations of software work, pointing to the need for organizations to differentiate between measures of…
Recently, we witness a rapid increase in the use of machine learning in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to…
Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As…
Platforms such as Stack Overflow are available for software practitioners to solicit solutions to their challenges and knowledge needs. The practices therein have in recent times however triggered quality related concerns. This is a…
In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that…
Context: Machine learning (ML)-enabled systems are being increasingly adopted by companies aiming to enhance their products and operational processes. Objective: This paper aims to deliver a comprehensive overview of the current status quo…
Context: Advancements in machine learning (ML) lead to a shift from the traditional view of software development, where algorithms are hard-coded by humans, to ML systems materialized through learning from data. Therefore, we need to…
The amount of aspect-oriented software development techniques and tools have been increasing for the last years but still they have not enough maturity and are not sufficiently spread to be included in a project leader's box of tools.…
The potential disconnect between research and practice in software engineering (SE) means that the uptake of research outcomes has at times been limited. In this paper we seek to identify research approaches that are rigorous in terms of…