1 citations · 1 across the 4 of their papers we have counts for
5 papers · 1 filter
Multi-Agent Coordinated Rename Refactoring
Abhiram Bellur, Mohammed Raihan Ullah, Fraol Batole +9
The primary value of AI agents in software development lies in their ability to extend the developer's capacity for reasoning and action, not to supplant human involvement. To show…
Leveraging LLMs, IDEs, and Semantic Embeddings for Automated Move Method Refactoring
Abhiram Bellur, Fraol Batole, Mohammed Raihan Ullah +12
MOVEMETHOD is a hallmark refactoring. Despite a plethora of research tools that recommend which methods to move and where, these recommendations do not align with how expert develo…
Mock Deep Testing: Toward Separate Development of Data and Models for Deep Learning
Ruchira Manke, Mohammad Wardat, Foutse Khomh +1
While deep learning (DL) has permeated, and become an integral component of many critical software systems, today software engineering research hasn't explored how to separately te…
Leveraging Data Characteristics for Bug Localization in Deep Learning Programs
Ruchira Manke, Mohammad Wardat, Foutse Khomh +1
Deep Learning (DL) is a class of machine learning algorithms that are used in a wide variety of applications. Like any software system, DL programs can have bugs. To support bug lo…
muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
Deepak-George Thomas, Matteo Biagiola, Nargiz Humbatova +4
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid rob…