相关论文: Implementing Large-Scale Agile Frameworks: Challen…

This action research study focuses on the integration of "AI assistants" in two Agile software development meetings: the Daily Scrum and a feature refinement, a planning meeting that is part of an in-house Scaled Agile framework. We discuss…

The intersection between security and continuous software engineering has been of great interest since the early years of the agile development movement, and it remains relevant as software development processes are more frequently guided…

Recommender Systems (RS) have become essential tools in a wide range of digital services, from e-commerce and streaming platforms to news and social media. As the volume of user-item interactions grows exponentially, especially in Big Data…

Context: Agile development methodologies in the software industry have increased significantly over the past decade. Although one of the main aspects of agile software development (ASD) is less documentation, there have always been…

In this paper, we discuss our experience in designing and teaching a course on Software Engineering Project Management, where the focus is on Agile/Scrum development and Requirement Engineering activities. The course has undergone…

Context: The combination of distributed stream processing with microservice architectures is an emerging pattern for building data-intensive software systems. In such systems, stream processing frameworks such as Apache Flink, Apache Kafka…

Agile methods are predominantly focused on delivering business values. But can Agile methods be adapted to effectively address and deliver human values such as social justice, privacy, and sustainability in the software they produce? Human…

Context: Companies adapt agile methods, practices or artifacts for their use in practice since more than two decades. This adaptions result in a wide variety of described agile practices. For instance, the Agile Alliance lists 75 different…

Through our four years experiments on students' Scrum based agile software development (ASD) process, we have gained deep understanding into the human factors of agile methodology. We designed an agile project management tool - the HASE…

There is a lack of formal risk management techniques in agile software development methods Scrum. The need to manage risks in agile project management is also identified by various authors. Authors of this paper conducted a survey to find…

Large language models (LLMs) have limitations in handling tasks that require real-time access to external APIs. While several benchmarks like ToolBench and APIGen have been developed to assess LLMs' API-use capabilities, they often suffer…

Companies implement different frameworks and best practices with the objective to improve the project management success rate and improve the business adaptability to the changing business environment. Project management framework (PRINCE2)…

Agile methods, especially Scrum, have become staples of the modern software development industry. Retrospective meetings are Scrum's instrument for process improvement and adaptation. They are considered one of the most important aspects of…

The Agile manifesto encourages us to value individuals and interactions over processes and tools, while Scrum, the most adopted Agile development methodology, is essentially based on roles, events, artifacts, and the rules that bind them…

The increasing pressure within VUCA (volatility, uncertainty, complexity and ambiguity) driven environments causes traditional, plan-driven Systems Engineering approaches to no longer suffice. Agility is then changing from a "nice-to-have"…

With the advent of large language models (LLMs) in the artificial intelligence (AI) area, the field of software engineering (SE) has also witnessed a paradigm shift. These models, by leveraging the power of deep learning and massive amounts…

While Large Language Models (LLMs) excel at tool calling, deploying these capabilities in regulated enterprise environments such as fintech presents unique challenges due to on-premises constraints, regulatory compliance requirements, and…

Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house…

The number of machine learning, artificial intelligence or data science related software engineering projects using Agile methodology is increasing. However, there are very few studies on how such projects work in practice. In this paper,…

Nowadays, the use of agile software development methods like Scrum is common in industry and academia. Considering the current attacking landscape, it is clear that developing secure software should be a main concern in all software…