Enhancing REST API Testing with NLP Techniques

Author:

Kim Myeongsoo1,Corradini Davide2,Sinha Saurabh3,Orso Alessandro1,Pasqua Michele2,Tzoref-Brill Rachel4,Ceccato Mariano2

Affiliation:

1. Georgia Institute of Technology, USA

2. University of Verona, Italy

3. IBM Research, USA

4. IBM Research, Israel

Funder

European Union's Horizon Europe research and innovation programme

Italian Ministry of University and Research - PON programme for Research and Innovation (Action IV.6)

NSF (National Science Foundation)

DARPA

DOE U.S. Department of Energy

Publisher

ACM

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Leveraging Natural Language Processing and Data Mining to Augment and Validate APIs;Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis;2024-09-11

2. DynER: Optimized Test Case Generation for Representational State Transfer (REST)ful Application Programming Interface (API) Fuzzers Guided by Dynamic Error Responses;Electronics;2024-09-01

3. On the Impact of Input Models on the Fault Detection Capabilities of Combinatorial Testing;SN Computer Science;2024-08-27

4. Advanced White-Box Heuristics for Search-Based Fuzzing of REST APIs;ACM Transactions on Software Engineering and Methodology;2024-06-27

5. KAT: Dependency-Aware Automated API Testing with Large Language Models;2024 IEEE Conference on Software Testing, Verification and Validation (ICST);2024-05-27

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