10 citations · 14 across the 9 of their papers we have counts for
8 papers · 1 filter
Tangent: An Empirical Study of Testing Practices for LLM-Based Agent Applications
Rangeet Pan, Tyler Stennett, Divya Sankar +5
Agents built on large language models (LLMs) are increasingly used to build applications that perform complex, multi-step tasks involving reasoning, tool use, and interaction with…
CRANE: Constrained Reasoning Injection for Code Agents via Nullspace Editing
Mingzhi Zhu, Michele Merler, Raju Pavuluri +1
Code agents must both reason over long-horizon repository state and obey strict tool-use protocols. In paired Instruct/Thinking checkpoints, these capabilities are complementary bu…
ScarfBench: A Benchmark for Cross-Framework Application Migration in Enterprise Java
Advait Pavuluri, Bridget McGinn, Ashita Saxena +6
Java remains central to enterprise software, and many applications outlive their original architecture. Migrating them across frameworks is a behavior-preserving refactoring spanni…
Usage, Effects and Requirements for AI Coding Assistants in the Enterprise: An Empirical Study
Maja Vukovic, Rangeet Pan, Tin Kam Ho +3
The rise of large language models (LLMs) has accelerated the development of automated techniques and tools for supporting various software engineering tasks, e.g., program understa…
SAINT: Service-level Integration Test Generation with Program Analysis and LLM-based Agents
Rangeet Pan, Raju Pavuluri, Ruikai Huang +4
Enterprise applications are typically tested at multiple levels, with service-level testing playing an important role in validating application functionality. Existing service-leve…
Hamster: A Large-Scale Study and Characterization of Developer-Written Tests
Rangeet Pan, Tyler Stennett, Raju Pavuluri +3
Automated test generation (ATG), which aims to reduce the cost of manual test suite development, has been investigated for decades and has produced countless techniques based on a…
Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights
Rahul Krishna, Rangeet Pan, Raju Pavuluri +3
Large Language Models for Code (or code LLMs) are increasingly gaining popularity and capabilities, offering a wide array of functionalities such as code completion, code generatio…
ASTER: Natural and Multi-language Unit Test Generation with LLMs
Rangeet Pan, Myeongsoo Kim, Rahul Krishna +2
Implementing automated unit tests is an important but time-consuming activity in software development. To assist developers in this task, many techniques for automating unit test g…