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Recent advances in artificial intelligence (AI) have shown promise in automating key aspects of Agile project management, yet their impact on team cognition remains underexplored. In this work, we investigate cognitive offloading in Agile…
Requirements Engineering (RE) is one of the prime areas in software development. Since agile software development englobes several emerging techniques and advocates for continuous improvement, it urges the question of which agile RE…
Despite growing interest in using large language models (LLMs) to automate annotation, their effectiveness in complex, nuanced, and multi-dimensional labelling tasks remains relatively underexplored. This study focuses on annotation for the…
There has been some evidence that agility is connected to the group maturity of software development teams. This study aims at conducting group development psychology training with student teams, participating in a project course at…
Many see retrospectives as the most important practice of agile software development. Previous studies of retrospectives have focused on pro- cess and outcome at team level. In this article, we study how a large-scale agile development…
A good documentation is essential for a good usability of (security) APIs, i.e. especially for the correct use of the APIs. Requirements for good documentation of APIs have been described in several papers, but there is no technical…
Agile methods provide an organization or a team the flexibility to adopt a selected subset of principles and practices based on their culture, their values, and the types of systems that they develop. More specifically, every organization…
Information of many kinds is flowing in software projects and organizations. Requirements have to flow from the customer to the developers. Testers need to know the requirements as well. Boundary conditions and design decisions have to be…
Agile methods and associated practices have been held to deliver value to software developers and customers. Research studies have reported team productivity and software quality benefits. While such insights are helpful for understanding…
While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However, a task's data efficiency--i.e., the number of fine-tuning…
With the agile approach to managing software development projects comes an increased dependability on well functioning teams, since many of the practices are built on teamwork. The objective of this study was to investigate if, and how,…
Evaluation of potential AGI systems and methods is difficult due to the breadth of the engineering goal. We have no methods for perfect evaluation of the end state, and instead measure performance on small tests designed to provide…
While digital assistants are increasingly used to help with various productivity tasks, less attention has been paid to employing them in the domain of business documents. To build an agent that can handle users' information needs in this…
Agile software development evolves so rapidly that research struggles to remain timely and transferable - an issue heightened by the swift adoption of generative AI and agentic tools. Earlier discussions highlight theory and time gaps,…
Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Model (LLM) responses by leveraging relevant external documents during generation. Although previous studies noted that retrieving many documents can degrade…
Non-functional requirements (NFRs) are determinant for the success of software projects. However,they are characterized as hard to define, and in agile software development(ASD), are often given less priority and usually not documented. In…
Working with documents is a key part of almost any knowledge work, from contextualizing research in a literature review to reviewing legal precedent. Recently, as their capabilities have expanded, primarily text-based NLP systems have often…
The search task and the system both affect the demand on cognitive resources during information search. In some situations, the demands may become too high for a person. This article has a three-fold goal. First, it presents and critiques…
This paper shows that further evaluation metrics during model training are needed to decide about its applicability in inference. As an example, a LayoutLM-based model is trained for token classification in documents. The documents are…
Large amounts of annotated data have become more important than ever, especially since the rise of deep learning techniques. However, manual annotations are costly. We propose a tool that enables researchers to create large, high-quality,…