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Found 16 papers, 7 papers with code
Using LLMs in Generating Design Rationale for Software Architecture Decisions
Then, we selected five LLMs to generate DR for the architecture decisions with three prompting strategies, including zero-shot, chain of thought (CoT), and LLM-based agents.
On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices
The reasons for self-declaring AI-generated code include the need to track and monitor the code for future review and debugging, and ethical considerations.
Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair
The results show that while issue content is particularly effective in assisting LLMs with fault localization and program repair, different types of software artifacts complement each other.
Knowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review
Public Code Review (PCR) is an assistant to the internal code review of the development team, in the form of a public Software Question Answering (SQA) community, to help developers access high-quality and efficient review services.
Demystifying Issues, Causes and Solutions in LLM Open-Source Projects
To fill this research gap, we conducted an empirical study to understand the issues that practitioners encounter when developing and using LLM open-source software, the possible causes of these issues, and potential solutions.
Dual convolutional neural network with attention for image blind denoising
To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising.
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.
An Insight into Security Code Review with LLMs: Capabilities, Obstacles and Influential Factors
Security code review is a time-consuming and labor-intensive process typically requiring integration with automated security defect detection tools.
Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using Copilot
The results show that 8 out of 10 types of code smells can be detected in Copilot-generated Python code, among which Multiply-Nested Container is the most common one.
Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps
Our research focuses on AI-based mobile app reviews as the chance of unfair behaviors and outcomes in AI-based mobile apps may be higher than in non-AI-based apps.
An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks
Then we explored the performance of seven non-transformers based models (e. g., Support Vector Machine, Classification and Regression Trees), the ALBERT model, and three decoder-only models (i. e., ChatGPT, Claude, and Gemini) for 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.
