12 citations · 45 across the 11 of their papers we have counts for
11 papers
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…
A Survey on Query-based API Recommendation
Moshi Wei, Nima Shiri Harzevili, Alvine Boaye Belle +6
Application Programming Interfaces (APIs) are designed to help developers build software more effectively. Recommending the right APIs for specific tasks has gained increasing atte…
Assessing Evaluation Metrics for Neural Test Oracle Generation
Jiho Shin, Hadi Hemmati, Moshi Wei +1
In this work, we revisit existing oracle generation studies plus ChatGPT to empirically investigate the current standing of their performance in both NLG-based and test adequacy me…
SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models
Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili +4
We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans sou…
Dynamic Encoding and Decoding of Information for Split Learning in Mobile-Edge Computing: Leveraging Information Bottleneck Theory
Omar Alhussein, Moshi Wei, Arashmid Akhavain
Split learning is a privacy-preserving distributed learning paradigm in which an ML model (e.g., a neural network) is split into two parts (i.e., an encoder and a decoder). The enc…
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…
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…
Yet Another Combination of IR- and Neural-based Comment Generation
Huang Yuchao, Wei Moshi, Wang Song +2
Code comment generation techniques aim to generate natural language descriptions for source code. There are two orthogonal approaches for this task, i.e., information retrieval (IR…
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…
ENCORE: Ensemble Learning using Convolution Neural Machine Translation for Automatic Program Repair
Thibaud Lutellier, Lawrence Pang, Viet Hung Pham +2
Automated generate-and-validate (G&V) program repair techniques typically rely on hard-coded rules, only fix bugs following specific patterns, and are hard to adapt to different pr…