10 citations · 23 across the 12 of their papers we have counts for
12 papers
Investigating the Efficacy of Large Language Models for Code Clone Detection
Mohamad Khajezade, Jie JW Wu, Fatemeh Hendijani Fard +2
Large Language Models (LLMs) have demonstrated remarkable success in various natural language processing and software engineering tasks, such as code generation. The LLMs are mainl…
Model-Agnostic Syntactical Information for Pre-Trained Programming Language Models
Iman Saberi, Fatemeh H. Fard
Pre-trained Programming Language Models (PPLMs) achieved many recent states of the art results for many code-related software engineering tasks. Though some studies use data flow o…
On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules
Divyam Goel, Ramansh Grover, Fatemeh H. Fard
Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently used in software engineering as models pre-trained on large source code corpora. Their knowledge is transf…
On the Effectiveness of Pretrained Models for API Learning
Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Ferdian Thung +4
Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit f…
Evaluating few shot and Contrastive learning Methods for Code Clone Detection
Mohamad Khajezade, Fatemeh Hendijani Fard, Mohamed S. Shehata
Context: Code Clone Detection (CCD) is a software engineering task that is used for plagiarism detection, code search, and code comprehension. Recently, deep learning-based models…
Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation
Yue Cao, Fatemeh H. Fard
Studies show that developers' answers to the mobile app users' feedbacks on app stores can increase the apps' star rating. To help app developers generate answers that are related…