相关论文: An Empirical Investigation on the Challenges in Sc…
Virtual e-Science infrastructures supporting Web-based scientific workflows are an example for knowledge-intensive collaborative and weakly-structured processes where the interaction with the human scientists during process execution plays…
Scientific publishing seems to be at a turning point. Its paradigm has stayed basically the same for 300 years but is now challenged by the increasing volume of articles that makes it very hard for scientists to stay up to date in their…
Increased adoption of scientific workflows in the community has urged for the development of multi-tenant platforms that provide these workflow executions as a service. As a result, Workflow-as-a-Service (WaaS) concept has been created by…
The increasing availability of cloud computing services for science has changed the way scientific code can be developed, deployed, and run. Many modern scientific workflows are capable of running on cloud computing resources. Consequently,…
The cutting edge in systems development today is in the area of "systems of systems" (SoS) large networks of inter-related systems that are developed and managed separately, but that also perform collective activities. Such large systems…
In open-source software development environments; textual, numerical and relationship-based data generated are of interest to researchers. Various data sets are available for this data, which is frequently used in areas such as software…
Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are rapidly increasing in complexity. This gives rise to the need for scientific communities…
Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains, including chatbots, healthcare, education, content generation, and automated support systems. However, developers…
DevOps is a combination of methodologies and tools that improves the software development, build, deployment, and monitoring processes by shortening its lifecycle and improving software quality. Part of this process is CI/CD, which embodies…
Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional…
Efficient data management is a key component in achieving good performance for scientific workflows in distributed environments. Workflow applications typically communicate data between tasks using files. When tasks are distributed, these…
In recent years, the data science community has pursued excellence and made significant research efforts to develop advanced analytics, focusing on solving technical problems at the expense of organizational and socio-technical challenges.…
Artificial Intelligence systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech…
Software is now a vital scientific instrument, providing the tools for data collection and analysis across disciplines from bioinformatics and computational physics, to the humanities. The software used in research is often home-grown and…
Open source software is a rapidly evolving center for distributed work, and understanding the characteristics of this work across its different contexts is vital for informing policy, economics, and the design of enabling software. The…
Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and…
Usability is a crucial factor but one of the most neglected concerns in open source software (OSS). While far from an ideal approach, a common practice that OSS communities adopt to collaboratively address usability is through discussions…
Motivation: Building and iterating machine learning models is often a resource-intensive process. In biomedical research, scientific codebases can lack scalability and are not easily transferable to work beyond what they were intended.…
To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is…
Extending knowledge by identifying and investigating valuable research questions and problems is a core function of research. Research publications often suggest avenues for future work to extend and build upon their results. Considering…