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We have developed an open-source software package (SedSimple) for stratigraphic forward modeling. It is aimed for use in research and education. The package uses ASCII files for input and output to facilitate understanding, debugging and…
LLM-based agents have shown promising capabilities in a growing range of software engineering (SWE) tasks. However, advancing this field faces two critical challenges. First, high-quality training data is scarce, especially data that…
Business systems these days need to be agile to address the needs of a changing world. In particular the discipline of Enterprise Application Integration requires business process management to be highly reconfigurable with the ability to…
Background:Technical systems are growing in complexity with more components and functions across various disciplines. Model-Driven Engineering (MDE) helps manage this complexity by using models as key artifacts. Domain-Specific Languages…
Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore,…
Existing scientific literature highlights the importance of metrics in Agile Software Development (ASD). Still, empirical investigation into metrics in ASD is scarce, particularly in identifying the rationale and the operational challenges…
Important computational physics problems are often large-scale in nature, and it is highly desirable to have robust and high performing computational frameworks that can quickly address these problems. However, it is no trivial task to…
Engineering the software development process in robotics is one of the basic necessities towards industrial-strength service robotic systems. A major challenge is to make the step from code-driven to model-driven systems. This is essential…
An essential characteristic of mature software and system development organizations is the definition and use of explicit process models. For a number of reasons, it can be valuable to produce new process models by tailoring existing…
Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency,…
Artificial Intelligence (AI) or Machine Learning (ML) systems have been widely adopted as value propositions by companies in all industries in order to create or extend the services and products they offer. However, developing AI/ML systems…
Ensuring software quality remains a critical challenge in complex and dynamic development environments, where software defects can result in significant operational and financial risks. This paper proposes an innovative framework for…
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…
It seems logical to assert that the dynamic nature of software engineering practice would mean that software effort estimation (SEE) modelling should take into account project start and completion dates. That is, we should build models for…
Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location…
Large Language Models have significantly advanced the field of code generation, demonstrating the ability to produce functionally correct code snippets. However, advancements in generative AI for code overlook foundational Software…
The advent of Artificial intelligence has promising advantages that can be utilized to transform the landscape of software project development. The Software process framework consists of activities that constantly require routine human…
The unknown parameters of simulation models often need to be calibrated using observed data. When simulation models are expensive, calibration is usually carried out with an emulator. The effectiveness of the calibration process can be…
Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As…
Software Engineering practitioners work using highly diverse methods and practices, and general theories in software engineering are lacking. One attempt at creating a common ground in the area of software engineering methodologies has been…