相关论文: Exploring Data Management Challenges and Solutions…
The rapid advancement of software development practices has introduced challenges in ensuring quality and efficiency across the software engineering (SE) lifecycle. As SE systems grow in complexity, traditional approaches often fail to…
Retrospectives are vital for software development teams to continuously enhance their processes and teamwork. Despite the increasing availability of objective data generated throughout the project and software development processes, many…
Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as…
Context: Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners…
Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks. Derivative products, like ChatGPT, have been extensively deployed and highly sought after.…
Synthetic data generation using large language models (LLMs) demonstrates substantial promise in addressing biomedical data challenges and shows increasing adoption in biomedical research. This study systematically reviews recent advances…
Today, data guides the decision-making process of most companies. Effectively analyzing and manipulating data at scale to extract and exploit relevant knowledge is a challenging task, due to data characteristics such as its size, the rate…
Agile methods have gotten a good reputation for managing projects in many different sectors. A challenge among practitioners in the ERP (Enterprise Resource Planning) domain, is to decide if an agile method is suitable or not for new…
In the dynamic landscape of contemporary business, the wave in data and technological advancements has directed companies toward embracing data-driven decision-making processes. Despite the vast potential that data holds for strategic…
Authoring survey or review articles still requires significant tedious manual effort, despite many advancements in research knowledge management having the potential to improve efficiency, reproducibility, and reuse. However, these…
Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open…
Software systems are a significant contributor to global sustainability concerns, demanding that environmental, social, technical, and economic factors be systematically addressed from the initial requirements engineering phase. Although…
The increasing complexity of software systems has driven significant advancements in program analysis, as traditional methods unable to meet the demands of modern software development. To address these limitations, deep learning techniques,…
Context: The utility of prediction models in empirical software engineering (ESE) is heavily reliant on the quality of the data used in building those models. Several data quality challenges such as noise, incompleteness, outliers and…
While organizations want to develop software products with reduced cost and flexible scope, stories about the applicability of agile practices to improve project development and performance in the software industry are scarce and focused on…
Context: The rapid adoption of AI-assisted code generation tools, such as large language models (LLMs), is transforming software development practices. While these tools promise significant productivity gains, concerns regarding the…
Software testing has received much attention over the last years and has reached such critical importance that agile software development practices put software testing at its core. Agile software development is successfully applied in…
\textit{Background:} The use of large language models in software testing is growing fast as they support numerous tasks, from test case generation to automation, and documentation. However, their adoption often relies on informal…
Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise…
Large Language Models (LLMs) are used for many different software engineering tasks. In software architecture, they have been applied to tasks such as classification of design decisions, detection of design patterns, and generation of…