PerfLearner: learning from bug reports to understand and generate performance test frames

Author:

Han Xue1,Yu Tingting1,Lo David2

Affiliation:

1. University of Kentucky, USA

2. Singapore Management University, Singapore

Publisher

ACM

Reference58 articles.

1. MoMS: Multi-objective miniaturization of software

2. A practical guide for using statistical tests to assess randomized algorithms in software engineering

3. Beautiful soup 2017. https://www.crummy.com/software/BeautifulSoup/. Beautiful soup 2017. https://www.crummy.com/software/BeautifulSoup/.

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1. Identifying Performance Issues in Microservice Architectures through Causal Reasoning;Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024);2024-04-15

2. Extracting Relevant Test Inputs from Bug Reports for Automatic Test Case Generation;Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings;2024-04-14

3. Gas‐centered mutation testing of Ethereum Smart Contracts;Journal of Software: Evolution and Process;2024-04-12

4. When Database Meets New Storage Devices: Understanding and Exposing Performance Mismatches via Configurations;Proceedings of the VLDB Endowment;2023-03

5. Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learning Model;IEEE Access;2023

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