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# System prepended metadata

title: Katalon SER Summary

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# Katalon SER Summary

## Paper

### 2023
- [ChatUniTest: a ChatGPT-based automated unit test
generation tool](https://arxiv.org/pdf/2305.04764v1.pdf) - Zhejiang University

### 2022
- [Leveraging Automated Unit Tests for
Unsupervised Code Translation](https://arxiv.org/pdf/2110.06773v2.pdf) - Meta AI
- [Automated Test Generation for REST APIs: No Time to Rest Yet](https://arxiv.org/pdf/2204.08348v3.pdf) - Georgia Tech
    > The authors identified a set of 10 state-of-the-art REST API testing tools that included tools developed by both researchers and practitioners. The tools are: EvoMasterWB, APIFuzzer, bBOXRT, Dredd, EvoMasterBB, RESTest, RESTler, RestTestGen, Schemathesis, Tcases. These tools then are identified to a benchmark of 20 real-world open-source RESTful services and analyzed their performance in terms of code coverage achieved and unique failures triggered. They found that all tools achieved relatively low line and branch coverage on many benchmarks, which indicates that there is considerable room for improvement. Two common limitations of many tools involve the inability of generating input values that satisfy specific constraints (e.g., parameters that must have a given format), and satisfying dependencies among requests (e.g., this endpoint must be called before these other endpoints). In general, they found that accounting for dependencies among endpoints is key to performing effective REST API testing, but existing techniques either do not consider these dependencies or use weak heuristics to infer them, which limits their overall effectiveness. The paper also discusses lessons learned and implications for future research based on their results.
- [Automated Testing with Machine Learning Frameworks: A Critical Analysis](https://www.mdpi.com/2673-4591/20/1/12)
- [DEAR: A Novel Deep Learning-based Approach
for Automated Program Repair](https://arxiv.org/pdf/2205.01859.pdf) - NJIT

### 2021
- [Supervised Learning for Test Suit Selection in
Continuous Integration](https://sci-hub.se/10.1109/ICSTW52544.2021.00048) - University of Lisbon
- [AI-based Test Automation: A Grey Literature
Analysis](https://tsigalko18.github.io/assets/pdf/2021-Ricca-ICSTW.pdf) - Italy
- [CodeNet: A Large-Scale AI for Code Dataset for
Learning a Diversity of Coding Tasks](https://arxiv.org/pdf/2105.12655.pdf) - MIT-IBM Lab
- [Automatic Web Testing Using Curiosity-Driven
Reinforcement Learning](https://arxiv.org/pdf/2103.06018.pdf)

## Blog
- [The power of Automation and AI on API Testing](https://www.ibm.com/blog/the-power-of-automation-and-ai-on-api-testing/) - IBM
- [Machine Learning in Software Testing](https://www.functionize.com/machine-learning-in-software-testing) - Functionize
- [AI in Test Automation](https://theqalead.com/automation-ai/ai-test-automation/) - QA Lead