8 papers
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…
AutoRestTest at the SBFT 2026 Tool Competition
Tyler Stennett, Myeongsoo Kim, Saurabh Sinha +1
Large input spaces and complex inter-operation dependencies make black-box REST API testing challenging. AutoRestTest combines a Semantic Property Dependency Graph, multi-agent rei…
Sakura: An Approach for Generating Complex Tests from Natural Language Test Descriptions
Tyler Stennett, Rangeet Pan, Bridget McGinn +2
Research on automating software testing has spanned several decades. Most existing approaches generate unit tests for individual methods, validate isolated API endpoints, or target…
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…
LlamaRestTest: Effective REST API Testing with Small Language Models
Myeongsoo Kim, Saurabh Sinha, Alessandro Orso
Modern web services rely heavily on REST APIs, typically documented using the OpenAPI specification. The widespread adoption of this standard has resulted in the development of man…