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Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional…
Open-source software (OSS) development relies on effective collaboration among distributed contributors. Yet, current OSS project recommendation systems primarily emphasize technical attributes, overlooking the collaboration and community…
Release management is one of the most important software processes and is a set of processes that includes the compilation, configuration, and management of software versions in different environments. In recent years, changes in processes,…
Despite many works on collaborative networked organizations (CNOs), CSCW, groupware, workflow systems and social networks, computer support for virtual teams is still insufficient, especially support for agility, i.e. the capability of…
Background: Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, which require less time and information than other formal…
Automotive companies increasingly adopt scaled agile methods to allow them to deal with their organisational and product complexity. Suitable methods are needed to ensure safety when developing automotive systems. On a small scale, R-Scrum…
Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help…
Software design is gradually becoming open, distributed, pervasive, and connected. It is a sad statistical fact that software projects are scientifically fragile and tend to fail more than other engineering fields. Agile development is a…
Traditionally, software quality is thought to depend on sound software engineering and development methodologies such as structured programming and agile development. However, high quality software depends just as much on high quality…
Large Language Models have quickly become a central component of modern software development workflows, and software practitioners are increasingly integrating LLMs into various stages of the software development lifecycle. Despite the…
This article explores Multimodal Analytics' use in assessing communication within agile software development, particularly through planning poker, to understand collaborative behavior. Multimodal Analytics examines verbal, paraverbal, and…
Agile practices are receiving considerable attention from industry as an alternative to traditional software development approaches. However, there are a number of challenges in combining Agile [2] with Test-driven development (TDD) [10]…
The rapid evolution of IT projects has driven the transformation of project management methodologies, from traditional waterfall approaches to agile frameworks and, more recently, hybrid models. This systematic literature review…
Large language model (LLM)-based agents that reason, plan, and act through tools, memory, and structured interaction are emerging as a promising paradigm for automating complex workflows. Recent systems such as OpenClaw and Claude Code…
Despite the popularity of Agile software development, achieving consistent project success remains challenging. This systematic literature review identifies critical success factors (CSFs) in Agile projects by analyzing 53 primary studies.…
Remote collaboration systems have become increasingly important in today's society, especially during times where physical distancing is advised. Industry, research and individuals face the challenging task of collaborating and networking…
Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs)…
[Context] Machine learning (ML)-enabled systems are present in our society, driving significant digital transformations. The dynamic nature of ML development, characterized by experimental cycles and rapid changes in data, poses challenges…
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodology in place to do so. To fill this gap, we propose MLTE…
Scientific workflows are powerful tools for management of scalable experiments, often composed of complex tasks running on distributed resources. Existing cyberinfrastructure provides components that can be utilized within repeatable…