6 papers
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…
Process-Centric Analysis of Agentic Software Systems
Shuyang Liu, Yang Chen, Rahul Krishna +3
Agentic systems are modern software systems: they consist of orchestrated modules, expose interfaces, and are deployed in software pipelines. Unlike conventional programs, their ex…
Multi-task Code LLMs: Data Mix or Model Merge?
Mingzhi Zhu, Boris Sobolev, Rahul Krishna +3
Recent research advocates deploying smaller, specialized code LLMs in agentic frameworks alongside frontier models, sparking interest in efficient strategies for multi-task learnin…
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…
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…
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…