61 citations · 154 across the 41 of their papers we have counts for
7 papers · 1 filter
Selecting Source Code Generation Tools Based on Bandit Algorithms
Ryoto Shima, Masateru Tsunoda, Yukasa Murakami +5
Background: Recently, code generation tools such as ChatGPT have drawn attention to their performance. Generally, a prior analysis of their performance is needed to select new code…
Architecture Decisions in Quantum Software Systems: An Empirical Study on Stack Exchange and GitHub
Mst Shamima Aktar, Peng Liang, Muhammad Waseem +4
Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for cl…
Assessing the Security of GitHub Copilot Generated Code -- A Targeted Replication Study
Vahid Majdinasab, Michael Joshua Bishop, Shawn Rasheed +3
AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on lar…
Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study
Yujia Fu, Peng Liang, Amjed Tahir +4
Modern code generation tools utilizing AI models like Large Language Models (LLMs) have gained increased popularity due to their ability to produce functional code. However, their…
Software Defect Prediction by Online Learning Considering Defect Overlooking
Yuta Yamasaki, Nikolay Fedorov, Masateru Tsunoda +5
Building defect prediction models based on online learning can enhance prediction accuracy. It continuously rebuilds a new prediction model when adding a new data point. However, p…
Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities
Jiaxin Yu, Liming Fu, Peng Liang +2
Background: Despite the widespread use of automated security defect detection tools, software projects still contain many security defects that could result in serious damage. Such…
On the Effect of Instrumentation on Test Flakiness
Shawn Rasheed, Jens Dietrich, Amjed Tahir
Test flakiness is a problem that affects testing and processes that rely on it. Several factors cause or influence the flakiness of test outcomes. Test execution order, randomness…