Cite this DOI

Natarajan, D. R. (2020). AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing. *Journal of Science & Technology*, *05*(05), 253–268. https://doi.org/10.46243/jst.2020.v5.i5.pp253-268

@article{natarajan2020aigenerated,
  author    = {Natarajan, Durai Rajesh},
  title     = {{AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing}},
  journal   = {Journal of Science \& Technology},
  year      = {2020},
  month     = {oct},
  volume    = {05},
  number    = {05},
  pages     = {253--268},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2020.v5.i5.pp253-268},
  url       = {https://doi.org/10.46243/jst.2020.v5.i5.pp253-268},
  language  = {en},
  abstract  = {Test automation must be intelligent, scalable, and efficient due to the growing complexity of software systems. With the use of machine learning (ML), natural language processing (NLP), and reinforcement learning (RL), this study offers an AI-Generated Test Automation for Autonomous Software Verification that maximizes test case creation, defect detection, and execution speed. The suggested framework reduces execution time (110.7 ms) and resource use (310.5 MB) while improving test coverage (94.8\%), defect detection rate (91.2\%), and correctness (96.7\%). The AI-driven method ensures minimal human interaction by automating the production of test cases, self-healing test scripts, and adapting to changing software modifications. The Full Model (Base + ML + NLP + RL) is the most effective method, with 98.2\% test coverage, 99.4\% accuracy, and 95.4 ms execution time, according to performance comparisons of ML-based, NLP-based, RL-based, and combined AI-driven automation. According to the ablation study, hybrid AI models perform better than solo techniques in terms of fault discovery, testing effectiveness, and verification accuracy.}
}

TY  - JOUR
TI  - AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing
AU  - Natarajan, Durai Rajesh
JO  - Journal of Science & Technology
PY  - 2020
DA  - 2020/10/29/
VL  - 05
IS  - 05
SP  - 253
EP  - 268
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Test automation must be intelligent, scalable, and efficient due to the growing complexity of software systems. With the use of machine learning (ML), natural language processing (NLP), and reinforcement learning (RL), this study offers an AI-Generated Test Automation for Autonomous Software Verification that maximizes test case creation, defect detection, and execution speed. The suggested framework reduces execution time (110.7 ms) and resource use (310.5 MB) while improving test coverage (94.8%), defect detection rate (91.2%), and correctness (96.7%). The AI-driven method ensures minimal human interaction by automating the production of test cases, self-healing test scripts, and adapting to changing software modifications. The Full Model (Base + ML + NLP + RL) is the most effective method, with 98.2% test coverage, 99.4% accuracy, and 95.4 ms execution time, according to performance comparisons of ML-based, NLP-based, RL-based, and combined AI-driven automation. According to the ablation study, hybrid AI models perform better than solo techniques in terms of fault discovery, testing effectiveness, and verification accuracy.
DO  - 10.46243/jst.2020.v5.i5.pp253-268
UR  - https://doi.org/10.46243/jst.2020.v5.i5.pp253-268
ER  -

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    "title": "AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing",
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    "abstract": "Test automation must be intelligent, scalable, and efficient due to the growing complexity of software systems. With the use of machine learning (ML), natural language processing (NLP), and reinforcement learning (RL), this study offers an AI-Generated Test Automation for Autonomous Software Verification that maximizes test case creation, defect detection, and execution speed. The suggested framework reduces execution time (110.7 ms) and resource use (310.5 MB) while improving test coverage (94.8%), defect detection rate (91.2%), and correctness (96.7%). The AI-driven method ensures minimal human interaction by automating the production of test cases, self-healing test scripts, and adapting to changing software modifications. The Full Model (Base + ML + NLP + RL) is the most effective method, with 98.2% test coverage, 99.4% accuracy, and 95.4 ms execution time, according to performance comparisons of ML-based, NLP-based, RL-based, and combined AI-driven automation. According to the ablation study, hybrid AI models perform better than solo techniques in terms of fault discovery, testing effectiveness, and verification accuracy.",
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2020.v5.i5.pp253-268 gives all four in one JSON answer.