Related papers: Proceedings 7th International Workshop on Formal M…
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…
Background: Sustainable software engineering (SSE) means creating software in a way that meets present needs without undermining our collective capacity to meet our future needs. It is typically conceptualized as several intersecting…
Issue resolution, a complex Software Engineering (SWE) task integral to real-world development, has emerged as a compelling challenge for artificial intelligence. The establishment of benchmarks like SWE-bench revealed this task as…
Recent advancements in natural language processing (NLP) have enabled the development of automated tools that support various domains, including software engineering. However, while NLP and artificial intelligence (AI) research has…
This volume contains the proceedings of MARS 2024, the sixth workshop on Models for Formal Analysis of Real Systems, held as part of ETAPS 2024, the European Joint Conferences on Theory and Practice of Software. The MARS workshops bring…
Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development…
Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a…
This volume contains the proceedings of F-IDE 2021, the sixth international workshop on Formal Integrated Development Environment, which was held online on May 24-25, 2021, as part of NFM'21, the 13th NASA Formal Methods Symposium. High…
Today's small and medium-sized enterprises (SMEs) in the software industry are faced with major challenges. While having to work efficiently using limited resources they have to perform quality assurance on their code to avoid the risk of…
Safety-critical software systems are in many cases designed and implemented as families of products, usually referred to as Software Product Lines (SPLs). Products within an SPL vary from each other in terms of which features they include.…
Secure software engineering is a fundamental activity in modern software development. However, while the field of security research has been advancing quite fast, in practice, there is still a vast knowledge gap between the security experts…
In this paper, we present our experience based on a reengineering project. The software project is to re-engineer the original system of a company to answer the new requirements and changed business functions. Reengineering is a process…
Software engineering concepts and processes are worthy of formal study; and yet we seldom formalize them. This "research ideas" article explores what a theory of software engineering could and should look like. Software engineering research…
Software engineering research has experienced rapid growth in both output and participation over the past decades. Yet concerns persist about the field's ability to accumulate, integrate, and reuse knowledge in ways that support long-term…
While using formal methods offers advantages over unit testing, their steep learning curve can be daunting to developers and can be a major impediment to widespread adoption. To support integration into an industrial software engineering…
Modern software systems undergo frequent updates, continuously evolving with new versions and variants to offer new features, improve functionality, and expand usability. Given the rapid pace of software evolution, organizations require…
Machine-learning (ML) techniques have become popular in the recent years. ML techniques rely on mathematics and on software engineering. Researchers and practitioners studying best practices for designing ML application systems and software…
The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice…
Empirical software engineering is concerned with the design and analysis of empirical studies that include software products, processes, and resources. Optimization is a form of data analytics in support of human decision-making.…
While modern Requirements Engineering (RE) heavily relies on natural language processing and Machine Learning (ML) techniques, their effectiveness is limited by the scarcity of high-quality datasets. This paper introduces Synthline, a…