相关论文: Understanding Open Source Contributor Profiles in …
Computer science educators are increasingly integrating open source contributions into classes to prepare students for higher expectations due to GenAI, and to improve employment outcomes in an increasingly competitive job market. However,…
Open Source Software (OSS) sustainability relies on newcomers transitioning to core contributors, but this pipeline is broken, with most newcomers becoming inactive after initial contributions. Open Source Software for Social Good (OSS4SG)…
Logs are an essential source of information for people to understand the running status of a software system. Due to the evolving modern software architecture and maintenance methods, more research efforts have been devoted to automated log…
Open-source developers, particularly the elite developers, maintain a diverse portfolio of contributing activities. They do not only commit source code but also spend a significant amount of effort on other communicative, organizational,…
README and CONTRIBUTING files can serve as the first point of contact for potential contributors to free/libre and open source software (FLOSS) projects. Prominent open source software organizations such as Mozilla, GitHub, and the Linux…
Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engineers and researchers who developed the models and the…
Mentoring is traditionally viewed as a dyadic, top-down apprenticeship. This perspective, however, overlooks other forms of informal mentoring taking place in everyday activities in which developers invest time and effort, but remain…
Software development increasingly depends on libraries and frameworks to increase productivity and reduce time-to-market. Despite this fact, we still lack techniques to assess developers expertise in widely popular libraries and frameworks.…
The rise of machine learning (ML) and its integration into software systems has drastically changed development practices. While software engineering traditionally focused on manually created code artifacts with dedicated processes and…
The sources of reliable, code-level information about vulnerabilities that affect open-source software (OSS) are scarce, which hinders a broad adoption of advanced tools that provide code-level detection and assessment of vulnerable OSS…
Knowledge about the roles developers play in a software project is crucial to understanding the project's collaborative dynamics. Developers are often classified according to the dichotomy of core and peripheral roles. Typically,…
Machine learning (ML) models are often integrated into ML-enabled systems to provide software functionality that would otherwise be impossible. This integration requires the selection of an appropriate ML model export format, for which many…
Faced with over 100M open source projects most empirical investigations select a subset. Most research papers in leading venues investigated filtering projects by some measure of popularity with explicit or implicit arguments that unpopular…
Practitioners from diverse occupations and backgrounds are increasingly using machine learning (ML) methods. Nonetheless, studies on ML Practitioners typically draw populations from Big Tech and academia, as researchers have easier access…
Integrating third-party software components is a common practice in modern software development, offering significant advantages in terms of efficiency and innovation. However, this practice is fraught with risks related to software…
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…
Given the increasing adoption of AI solutions in professional environments, it is necessary for developers to be able to make informed decisions about the current tool landscape. This work empirically evaluates various MLOps (Machine…
The open source development model has become a paradigm shift from traditional in-house/closed-source software development model, with many successes. Traditionally, open source projects were characterized essentially by their individual…
Attracting and retaining a steady stream of new contributors is crucial to ensuring the long-term survival of open-source software (OSS) projects. However, there are two key research gaps regarding recommendations for onboarding new…
Machine Learning (ML) has become ubiquitous, fueling data-driven applications across various organizations. Contrary to the traditional perception of ML in research, ML workflows can be complex, resource-intensive, and time-consuming.…