Ryeonggu Kwon

dblp:366/4819 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2024
0000-0002-4942-247XORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 3 · 3 since 2021
YearPublicationVenuePosition
2024 Assessing Open Source Software Survivability using Kaplan-Meier Survival Function and Polynomial Regression
abstract
This study evaluates OSS project survivability using the Kaplan-Meier Survival Function and polynomial regression models. The key factors identified include the number of contributors and project popularity, which significantly influence survivability. Traditional indicators like project age do not directly correlate with OSS survivability. Instead, community engagement and recognition are crucial, offering valuable guidelines for managing and selecting Survivable OSS projects.
Ryeonggu Kwon, Gihwon Kwon
ASE2
2023 Exploring Loss Scenarios of STPA with Reinforcement Learning: A Case Study of Platform Screen Door
abstract
System-Theoretic Process Analysis (STPA) is performing a potential hazard analysis in the interactions of components within a system. Among STPA's process, deriving loss scenarios by identifying causes of the hazard has become increasingly important due to the complexity of modern systems. In this study, we utilize reinforcement learning to derive state transition paths that lead to hazard as loss scenarios, and extract the frequency of risk frequencies to demonstrate the necessity of safety measures for the respective loss scenarios. It is anticipated that this efficient approach will aid in simulating the system design process and enhancing safety.
Jiyoung Chang, Ryeonggu Kwon, Gihwon Kwon
APSEC2
2023 Exploring Collaboration Patterns in GitHub Using Discrete Time Markov Chain
abstract
Collaboration among team members plays a crucial role in the success of software projects. GitHub has emerged as a prominent platform for collaborative software development. This study focuses on analyzing GitHub repositories to gain insights into project status and participant characteristics based on development activities such as commits and pull requests. Utilizing a discrete time Markov chain, this research models GitHub repositories and employs probabilistic temporal logic and model checking to shed light on the activity levels of both projects and participants. We expect that understanding these observed trends and patterns can assist project managers in strengthening teamwork and fostering productive collaboration.
Suhee Jo, Ryeonggu Kwon, Gihwon Kwon
APSEC2