6 citations · 6 across the 5 of their papers we have counts for
7 papers
MANTRA: a Framework for Multi-stage Adaptive Noise TReAtment During Training
Zixiao Zhao, Fatemeh H. Fard, Jie JW Wu
The reliable application of deep learning models to software engineering tasks hinges on high-quality training data. Yet, large-scale repositories inevitably introduce noisy or mis…
A Low-Cost Machine Learning Approach for Timber Diameter Estimation
Fatemeh Hasanzadeh Fard, Sanaz Hasanzadeh Fard, Mehdi Jonoobi
The wood processing industry, particularly in facilities such as sawmills and MDF production lines, requires accurate and efficient identification of species and thickness of the w…
Can Code Language Models Learn Clarification-Seeking Behaviors?
Jie JW Wu, Manav Chaudhary, Davit Abrahamyan +3
Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, a gap remains between their output and the problem-solving strategies of h…
A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages
Sathvik Joel, Jie JW Wu, Fatemeh H. Fard
Large Language Models (LLMs) have shown impressive capabilities in code generation for popular programming languages. However, their performance on Low-Resource Programming Languag…
MergeRepair: An Exploratory Study on Merging Task-Specific Adapters in Code LLMs for Automated Program Repair
Meghdad Dehghan, Jie JW Wu, Fatemeh H. Fard +1
Large Language Models (LLMs) have shown high capabilities in several software development-related tasks such as program repair, documentation, code refactoring, debugging, and test…
StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation
Davit Abrahamyan, Fatemeh H. Fard
Developers spend much time finding information that is relevant to their questions. Stack Overflow has been the leading resource, and with the advent of Large Language Models (LLMs…