Related papers: Data-Driven Search-based Software Engineering
Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the…
Context: The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. Objective: Examine the usage of data papers…
This paper introduces prompted software engineering (PSE), which integrates prompt engineering to build effective prompts for language-based AI models, to enhance the software development process. PSE enables the use of AI models in…
Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML)…
High-quality data has become increasingly important to software engineers in designing and implementing today's software, for example, as an input to machine-learning algorithms and visualisation- and analytics-based features. Open data -…
Several important aspects of software product quality can be evaluated using dynamic metrics that effectively capture and reflect the software's true runtime behavior. While the extent of research in this field is still relatively limited,…
Software engineering research is evolving and papers are increasingly based on empirical data from a multitude of sources, using statistical tests to determine if and to what degree empirical evidence supports their hypotheses. To…
Machine Learning software documentation is different from most of the documentations that were studied in software engineering research. Often, the users of these documentations are not software experts. The increasing interest in using…
Cloud computing has become a powerful and indispensable technology for complex, high performance and scalable computation. The exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety…
Cloud computing is emerging as a revolutionary computing paradigm which pro-vides a flexible and economic strategy for data management and resource sharing. Security and privacy become major concerns in the cloud scenario, for which…
The evidence-based approach has increasingly been employed to synthesize empirical findings from the primary research in software engineering. Nevertheless, the reproducibility of evidence-based software engineering (EBSE) studies seems to…
Search-based Software Engineering has been utilized for a number of software engineering activities. One area where Search-Based Software Engineering has seen much application is test data generation. Evolutionary testing designates the use…
Empirical and LLM-based research in model-driven engineering increasingly relies on datasets of software models, for instance, to train or evaluate machine learning techniques for modeling support. These datasets have a significant impact…
Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of data and the size and complexity of the statistical models used…
Programming and software engineering courses in computer science curricula typically focus on both providing theoretical knowledge of programming languages and best-practices, and developing practical development skills. In a massive course…
While Sustainable Software Engineering (SSE) tools are widely studied in academia, their practical feasibility in industrial workflows, particularly in regulated environments, remains poorly understood. This study investigates how software…
Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning…
In recent years, Machine Learning (ML) components have been increasingly integrated into the core systems of organizations. Engineering such systems presents various challenges from both a theoretical and practical perspective. One of the…
Emerging technologies and business models require organisations to continuously deal with complex, dynamic and unstructured issues, leading to the need for newer forms of decision support systems (DSS). However, in emerging environments the…
With the increase of research in self-adaptive systems, there is a need to better understand the way research contributions are evaluated. Such insights will support researchers to better compare new findings when developing new knowledge…