4 citations · 6 across the 14 of their papers we have counts for
12 papers · 1 filter
A-JIT: Agentic Just-In-Time Software Construction
Mark Marron, Earl T. Barr
Traditional software delivery assumes a static paradigm: code is constructed prior to execution and deployed as a fixed artifact. We present Agentic Just-In-Time Software Construct…
Evaluating Inference-Time Defenses Against Package Hallucination in LLM-Generated Code
Alberick Euraste Djire, Iyiola E. Olatunji, Melissa Tessa +3
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We…
Failure as a Process: An Anatomy of CLI Coding Agent Trajectories
Xiangxin Zhao, Han Li, Shuaiting Li +4
Large language model (LLM) coding agents are increasingly deployed to autonomously perform software engineering tasks in terminal-based environments, making their reliability a gro…
Hallucination Inspector: A Fact-Checking Judge for API Migration
Marcos Tileria, Santanu Kumar Dash, Profir-Petru Pârţachi +1
Large Language Models (LLMs) are increasingly deployed in automated software engineering for tasks such as API migration. While LLMs are able to identify migration patterns, they o…
Learned or Memorized ? Quantifying Memorization Advantage in Code LLMs
Djiré Albérick Euraste, Kaboré Abdoul Kader, Jordan Samhi +3
The lack of transparency about code datasets used to train large language models (LLMs) makes it difficult to detect, evaluate, and mitigate data leakage. We present a perturbation…
How Robustly do LLMs Understand Execution Semantics?
Claudio Spiess, Prem Devanbu, Earl T. Barr
LLMs demonstrate remarkable reasoning capabilities, yet whether they utilize internal world models or rely on sophisticated pattern matching remains open. We study LLMs through the…
Compressing Code Context for LLM-based Issue Resolution
Haoxiang Jia, Earl T. Barr, Sergey Mechtaev
Large Language Models (LLMs) are now capable of resolving real-world GitHub issues. However, current approaches overapproximate the code context and suffer from two compounding pro…
LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study
Shuai Wang, Yinan Yu, Earl Barr +1
Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI co…
Forecasting Developer Environments with GenAI: A Research Perspective
Raula Gaikovina Kula, Christoph Treude, Xing Hu +28
Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ab…
Detecting Semantic Clones of Unseen Functionality
Konstantinos Kitsios, Francesco Sovrano, Earl T. Barr +1
Semantic code clone detection is the task of detecting whether two snippets of code implement the same functionality (e.g., Sort Array). Recently, many neural models achieved near-…
Independent Test Generation for RESTful APIs
S M Sadrul Islam Asif, James Chen, Kennett Puerto Diaz +2
Modern REST API testing relies on brittle sequences of calls to build system state. These multi-step tests suffer from non-determinism, poor scalability, and a "reachability tax" w…
The Fact Selection Problem in LLM-Based Program Repair
Nikhil Parasaram, Huijie Yan, Boyu Yang +5
Recent research has shown that incorporating bug-related facts, such as stack traces and GitHub issues, into prompts enhances the bug-fixing capabilities of large language models (…