10.46243/jst.2020.v5.i5.pp253-268 registered
AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing
Resolves to https://www.jst.org.in/index.php/pub/article/view/1199
Held by Longman Publishers (India) · prefix 10.46243 live · DOI address https://doi.org/10.46243/jst.2020.v5.i5.pp253-268
Registered 29 Sep 2026 via crossref · record version 2 · last change 30 Sep 2026, 12:00 AM · record sha256 dd2cb938ecbe97ef…
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JournalArticle — an article in a journal · Digital · Visual · en
AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing (PrincipalTitle)
Published 2020-10-29
Part of Journal of Science & Technology · ISSN 2456-5660 · volume 05 · issue 05 · pages 253–268
Agents
- Durai Rajesh Natarajan (author)
- Longman Publishers (publisher)
Identifiers DOI 10.46243/jst.2020.v5.i5.pp253-268
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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Each element by the standard's name (Annex B: reference elements, then administrative) and the Handbook's (in grey), read off the record above.
| Element | Value | In the record |
|---|---|---|
| DOI Name DOI name | 10.46243/jst.2020.v5.i5.pp253-268 | doi |
| Referent Type referentType | Creation | referent |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal | type |
| Referent Name(s) referentName(s) | AI-Generated Test Automation for Autonomous Software Verification: Enhancing Quality Assurance Through AI-Driven Testing (PrincipalTitle, en) | titles |
| Basic Metadata basicMetadata | author: Durai Rajesh Natarajan publisher: Longman Publishers published: 2020-10-29 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 05 · no. 05 · pp. 253–268 language: en form: Digital · Visual · Language | agents, dates, container, language, structural_type, modes, characters |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI | identifiers, relations (IsSameAs) |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy | record.source_agency (our code, ra_doi_name, for names issued here once appointed) |
| Created Date issueDate | 2026-09-18 | record.registered (when the DOI name was first registered) |
| relatedIdentifiers | none needed — the descriptive metadata is in this record | container, relations (only where the descriptive metadata lives at another identifier) |
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| # | When | What | By | Changes |
|---|---|---|---|---|
| 1 | 29 Sep 2026, 10:00 PM | register registered at Crossref; record read from api.crossref.org | Administrator (admin) | 125 fields set · sha256 8b02d20a2151… |
| 2 | 30 Sep 2026, 12:00 AM | update record re-read from api.crossref.org | Administrator (admin) | container.titles.0.value: |
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