Related papers: Quality in model-driven engineering: a tertiary st…
As complex software and systems development projects need models as an important planning, structuring and development technique, models now face issues resolved for software earlier: models need to be versioned, differences captured,…
It can be insightful to extend qualitative studies with a secondary quantitative analysis (where the former suggests insightful questions that the latter can answer). Documenting developer beliefs should be the start, not the end, of…
Machine Learning (ML) models are being increasingly employed for credit risk evaluation, with their effectiveness largely hinging on the quality of the input data. In this paper we investigate the impact of several data quality issues,…
Data quality describes the degree to which data meet specific requirements and are fit for use by humans and/or downstream tasks (e.g., artificial intelligence). Data quality can be assessed across multiple high-level concepts called…
In the software development process, model transformation is increasingly assimilated. However, systems being developed with model transformation sometimes grow in size and become complex. Meanwhile, the performance of model transformation…
Success and failure in software engineering are still among the least understood phenomena in the discipline. In a recent special journal issue on the topic, Mantyla et al. started discussing these topics from different angles; the authors…
[Context & Motivation] Adaptive systems are an important research area. The dominant reason for adaptivity in systems are changes in the environment. Thus, it is an important question how to model the environment and how to determine the…
The rapid advancement of Large Language Models (LLMs) is reshaping software engineering by profoundly influencing coding, documentation, and system maintenance practices. As these tools become deeply embedded in developers' daily workflows,…
With the sharp rise in software dependability and failure cost, high quality has been in great demand. However, guaranteeing high quality in software systems which have grown in size and complexity coupled with the constraints imposed on…
Software sustainability is a key multifaceted non-functional requirement that encompasses environmental, social, and economic concerns, yet its integration into the development of Machine Learning (ML)-enabled systems remains an open…
Researchers are increasingly recognizing the importance of human aspects in software development. Since qualitative methods are used to explore human behavior in-depth, we believe that studies using such methods will become more common.…
Experiments are a commonly used method of research in software engineering (SE). Researchers report their experiments following detailed guidelines. However, researchers do not, in the field of test-driven development (TDD) at least,…
Software product lines have recently been presented as one of the best promising improvements for the efficient software development. Different research works contribute supportive parameters and negotiations regarding the problems of…
Background: Assessing and communicating software engineering research can be challenging. Design science is recognized as an appropriate research paradigm for applied research but is seldom referred to in software engineering. Applying the…
Nowadays, systems containing components based on machine learning (ML) methods are becoming more widespread. In order to ensure the intended behavior of a software system, there are standards that define necessary quality aspects of the…
Machine learning models are essential tools in various domains, but their performance can degrade over time due to changes in data distribution or other factors. On one hand, detecting and addressing such degradations is crucial for…
Background: Cheating in university education is commonly described as context dependent and influenced by assessment design, institutional norms, and student interpretation. In software engineering education, programming oriented coursework…
Context: In the realm of software development, maintaining high software quality is a persistent challenge. However, this challenge is often impeded by the lack of comprehensive understanding of how specific code modifications influence…
In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state,…
The use of learning-based techniques to achieve automated software vulnerability detection has been of longstanding interest within the software security domain. These data-driven solutions are enabled by large software vulnerability…