24 citations · 67 across the 13 of their papers we have counts for
12 papers · 1 filter
BloomAPR: A Bloom's Taxonomy-based Framework for Assessing the Capabilities of LLM-Powered APR Solutions
Yinghang Ma, Jiho Shin, Leuson Da Silva +5
Recent advances in large language models (LLMs) have accelerated the development of AI-driven automated program repair (APR) solutions. However, these solutions are typically evalu…
SecVulEval: Benchmarking LLMs for Real-World C/C++ Vulnerability Detection
Md Basim Uddin Ahmed, Nima Shiri Harzevili, Jiho Shin +2
Large Language Models (LLMs) have shown promise in software engineering tasks, but evaluating their effectiveness in vulnerability detection is challenging due to the lack of high-…
Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy
Hamed Taherkhani, Jiho Shin, Muhammad Ammar Tahir +3
Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code. These tests are synthetically generated by LLMs. Ho…
Checker Bug Detection and Repair in Deep Learning Libraries
Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Jiho Shin +8
Checker bugs in Deep Learning (DL) libraries are critical yet not well-explored. These bugs are often concealed in the input validation and error-checking code of DL libraries and…
From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap
Gopi Krishnan Rajbahadur, Gustavo A. Oliva, Dayi Lin +2
The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware, software systems that integrate FM(s) as core components. While buil…
Retrieval-Augmented Test Generation: How Far Are We?
Jiho Shin, Nima Shiri Harzevili, Reem Aleithan +2
Retrieval Augmented Generation (RAG) has advanced software engineering tasks but remains underexplored in unit test generation. To bridge this gap, we investigate the efficacy of R…
Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Jiho Shin, Clark Tang, Tahmineh Mohati +3
The rapid advancements in large language models (LLMs) have greatly expanded the potential for automated code-related tasks. Two primary methodologies are used in this domain: prom…
Domain Adaptation for Code Model-based Unit Test Case Generation
Jiho Shin, Sepehr Hashtroudi, Hadi Hemmati +1
Recently, deep learning-based test case generation approaches have been proposed to automate the generation of unit test cases. In this study, we leverage Transformer-based code mo…
Automatic Static Bug Detection for Machine Learning Libraries: Are We There Yet?
Nima Shiri harzevili, Jiho Shin, Junjie Wang +2
Automatic detection of software bugs is a critical task in software security. Many static tools that can help detect bugs have been proposed. While these static bug detectors are m…
The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks
Jiho Shin, Moshi Wei, Junjie Wang +2
Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature a…
Characterizing and Understanding Software Security Vulnerabilities in Machine Learning Libraries
Nima Shiri Harzevili, Jiho Shin, Junjie Wang +1
The application of machine learning (ML) libraries has been tremendously increased in many domains, including autonomous driving systems, medical, and critical industries. Vulnerab…
Explainable Software Defect Prediction: Are We There Yet?
Jiho Shin, Reem Aleithan, Jaechang Nam +2
Explaining the prediction results of software defect prediction models is a challenging while practical task, which can provide useful information for developers to understand and…