1 citations · 3 across the 9 of their papers we have counts for
10 papers
Pattern-Guided Graph Synthesis for Suppressing Known Defects in DL Compiler Fuzzing
Qiong Feng, Xiaotian Ma, Di Cui +2
Fuzzing is effective at finding bugs in deep learning (DL) compilers, but existing fuzzers repeatedly trigger faults they have already uncovered. Current fuzzers diversify the gene…
CrossLangFuzzer: Differential Testing of Cross-Language JVM Compilers
Xiaotian Ma, Qiong Feng, Yongqiang Tian +2
Modern JVM software increasingly integrates multiple programming languages, such as Java, Kotlin, Groovy, and Scala, within a single application. Supporting such interoperability r…
DRReduce: Enhancing Syntax-Guided Program Reduction with Dependency Reconstruction
Qiong Feng, Xiaotian Ma, Yongqiang Tian +2
Program reduction is a technique for simplifying large, failure-inducing programs into minimal reproducible test cases. Language-specific tools such as CReduce achieve strong perfo…
Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
Syed Mohammad Kashif, Ruiyin Li, Peng Liang +4
New generation of AI coding tools, including AI-powered IDEs equipped with agentic capabilities, can generate code within the context of the project. These AI IDEs are increasingly…
FasterPy: An LLM-based Code Execution Efficiency Optimization Framework
Yue Wu, Minghao Han, Ruiyin Li +5
Code often suffers from performance bugs. These bugs necessitate the research and practice of code optimization. Traditional rule-based methods rely on manually designing and maint…
Finding Compiler Bugs through Cross-Language Code Generator and Differential Testing
Qiong Feng, Xiaotian Ma, Ziyuan Feng +3
Compilers play a central role in translating high-level code into executable programs, making their correctness essential for ensuring code safety and reliability. While extensive…
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Code Smell Detection
Beiqi Zhang, Peng Liang, Xin Zhou +5
Automated code smell detection faces persistent challenges due to the subjectivity of heuristic rules and the limited performance of traditional ML/DL models. While Large Language…
An Empirical Study of Kotlin-Java Cross-Dependency Issues and Their Detection
Qiong Feng, Xiaotian Ma, Jiayi Sheng +2
Since Google introduced Kotlin as an official programming language for developing Android apps in 2017, Kotlin has gained widespread adoption in Android development. The interopera…
Depends-Kotlin: A Cross-Language Kotlin Dependency Extractor
Qiong Feng, Xiaotian Ma, Huan Ji +2
Since Google introduced Kotlin as an official programming language for developing Android apps in 2017, Kotlin has gained widespread adoption in Android development. However, compa…
Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using Copilot
Beiqi Zhang, Peng Liang, Qiong Feng +2
As one of the most popular dynamic languages, Python experiences a decrease in readability and maintainability when code smells are present. Recent advancements in Large Language M…