2 citations · 4 across the 5 of their papers we have counts for
6 papers
How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair
Ramtin Ehsani, Irene Manotas, Saurabh Pujar +2
Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent. Even when provided with the same contextual inf…
Automated Customization of LLMs for Enterprise Code Repositories Using Semantic Scopes
Ulrich Finkler, Irene Manotas, Wei Zhang +3
Code completion (CC) is a task frequently used by developers when working in collaboration with LLM-based programming assistants. Despite the increased performance of LLMs on publi…
Code Reasoning for Software Engineering Tasks: A Survey and A Call to Action
Saurabh Pujar, Ira Ceka, Irene Manotas +3
The rise of large language models (LLMs) has led to dramatic improvements across a wide range of natural language tasks. Their performance on certain tasks can be further enhanced…
Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found
Irene Manotas, Octavian Popescu, Ngoc Phuoc An Vo +1
There are many recent advanced developments for the Text-to-SQL task, where the Picard model is one of the the top performing models as measured by the Spider dataset competition.…
Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management
Ngoc Phuoc An Vo, Irene Manotas, Octavian Popescu +2
Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes. One…
SECRET: Semantically Enhanced Classification of Real-world Tasks
Ayten Ozge Akmandor, Jorge Ortiz, Irene Manotas +2
Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to…