Search Results for author:
Found 10 papers, 4 papers with code
On Unified Prompt Tuning for Request Quality Assurance in Public Code Review
Public Code Review (PCR) can be implemented through a Software Question Answering (SQA) community, which facilitates high knowledge dissemination.
Security Code Review by LLMs: A Deep Dive into Responses
To explore the challenges of applying LLMs in practical code review for security defect detection, this study compared the detection performance of three state-of-the-art LLMs (Gemini Pro, GPT-4, and GPT-3. 5) under five prompts on 549 code files that contain security defects from real-world code reviews.
Copilot Refinement: Addressing Code Smells in Copilot-Generated Python Code
The results show that 8 out of 10 types of Python smells can be detected in Copilot-generated Python code, among which Multiply-Nested Container is the most common one.
A Study of Fairness Concerns in AI-based Mobile App Reviews
Finally, the manual analysis of 2, 248 app owners' responses to the fairness reviews identified six root causes (e. g., 'copyright issues') that app owners report to justify fairness concerns.
An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks
To overcome the issues of manually identifying assumptions in DL framework development, we constructed a new and largest dataset (i. e., AssuEval) of assumptions collected from the TensorFlow and Keras repositories on GitHub; explored the performance of seven traditional machine learning models (e. g., Support Vector Machine, Classification and Regression Trees), a popular DL model (i. e., ALBERT), and a large language model (i. e., ChatGPT) of identifying assumptions on the AssuEval dataset.
Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connection
The proposed DCBDNet consists of a noise estimation network and a dual convolutional neural network (CNN).
DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising
In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet.
Understanding Bugs in Multi-Language Deep Learning Frameworks
Third, we found that 28. 6%, 31. 4%, and 16. 0% of bugs in MXNet, PyTorch, and TensorFlow are MPL bugs, respectively; the PL combination of Python and C/C++ is most used in fixing more than 92% MPL bugs in all DLFs.
Towards Human-Bot Collaborative Software Architecting with ChatGPT
Architecting software-intensive systems can be a complex process.
Ethics of AI: A Systematic Literature Review of Principles and Challenges
Ethics in AI becomes a global topic of interest for both policymakers and academic researchers.
