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…
Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews
Mohammad Abdul Hadi, Fatemeh H. Fard
Context: Mobile app reviews written by users on app stores or social media are significant resources for app developers.Analyzing app reviews have proved to be useful for many area…
API2Com: On the Improvement of Automatically Generated Code Comments Using API Documentations
Ramin Shahbazi, Rishab Sharma, Fatemeh H. Fard
Code comments can help in program comprehension and are considered as important artifacts to help developers in software maintenance. However, the comments are mostly missing or ar…
Technical Debt in the Peer-Review Documentation of R Packages: a rOpenSci Case Study
Zadia Codabux, Melina Vidoni, Fatemeh H. Fard
Context: Technical Debt is a metaphor used to describe code that is "not quite right." Although TD studies have gained momentum, TD has yet to be studied as thoroughly in non-Objec…
AOBTM: Adaptive Online Biterm Topic Modeling for Version Sensitive Short-texts Analysis
Mohammad Abdul Hadi, Fatemeh H Fard
Analysis of mobile app reviews has shown its important role in requirement engineering, software maintenance and evolution of mobile apps. Mobile app developers check their users'…
Geo-Spatial Data Visualization and Critical Metrics Predictions for Canadian Elections
Mohammad Abdul Hadi, Fatemeh H Fard, Irene Vrbik
Open data published by various organizations is intended to make the data available to the public. All over the world, numerous organizations maintain a considerable number of open…
ReviewViz: Assisting Developers Perform Empirical Study on Energy Consumption Related Reviews for Mobile Applications
Mohammad Abdul Hadi, Fatemeh H Fard
Improving the energy efficiency of mobile applications is a topic that has gained a lot of attention recently. It has been addressed in a number of ways such as identifying energy…