66 citations · 148 across the 16 of their papers we have counts for
16 papers
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…
A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation
Mohammad Mahdi Mohajer, Alvine Boaye Belle, Nima Shiri harzevili +5
The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML…
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…
Software Testing with Large Language Models: Survey, Landscape, and Vision
Junjie Wang, Yuchao Huang, Chunyang Chen +3
Pre-trained large language models (LLMs) have recently emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle l…
Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge
Arunkaleeshwaran Narayanan, Nima Shiri harzevili, Junjie Wang +3
Many automatic unit test generation tools that can generate unit test cases with high coverage over a program have been proposed. However, most of these tools are ineffective on de…
A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning
Nima Shiri Harzevili, Alvine Boaye Belle, Junjie Wang +4
Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures t…
Security Knowledge-Guided Fuzzing of Deep Learning Libraries
Nima Shiri Harzevili, Mohammad Mahdi Mohajer, Moshi Wei +2
Recently, many Deep Learning fuzzers have been proposed for testing of DL libraries. However, they either perform unguided input generation (e.g., not considering the relationship…
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…
Developer-Intent Driven Code Comment Generation
Fangwen Mu, Xiao Chen, Lin Shi +2
Existing automatic code comment generators mainly focus on producing a general description of functionality for a given code snippet without considering developer intentions. Howev…
Are We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization
Lin Shi, Fangwen Mu, Xiao Chen +6
Code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on wi…
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…
An Approach to Detecting Bugs in Pattern-Based Bug Detectors
Junjie Wang, Yuchao Huang, Song Wang +1
Static bug finders have been widely-adopted by developers to find bugs in real world software projects. They leverage predefined heuristic static analysis rules to scan source code…
CoCoFuzzing: Testing Neural Code Models with Coverage-Guided Fuzzing
Moshi Wei, Yuchao Huang, Jinqiu Yang +2
Deep learning-based code processing models have shown good performance for tasks such as predicting method names, summarizing programs, and comment generation. However, despite the…
Characterizing and Understanding Software Developer Networks in Security Development
Song Wang, Nachi Nagappan
To build secure software, developers often work together during software development and maintenance to find, fix, and prevent security vulnerabilities. Examining the nature of dev…
Cutting Away the Confusion From Crowdtesting
Junjie Wang, Mingyang Li, Song Wang +2
Crowdtesting is effective especially when it comes to the feedback on GUI systems, or subjective opinions about features. Despite of this, we find crowdtesting reports are highly r…