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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Engineering Quality in the Age of AI: A Lifecycle Framework for Reliable, Interpretable and Verifiable AI Systems |
| Authors Name: | Rajeew Vishvakarma |
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| Author Reg. ID: |
IJRTI_211759
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| Published Paper Id: | IJRTI2605158 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | Traditional software assurance assumes that system behavior is defined by explicit logic and relatively stable requirements. In AI-enabled systems, these assumptions weaken because quality depends on data representativeness, probabilistic outputs, post-deployment drift, limited interpretability, and context-sensitive performance. At the same time, AI is increasingly used inside testing, defect analysis, triage, and release workflows, so both AI-enabled products and AI-supported quality processes require assurance. This paper proposes a unified, lifecycle-oriented framework for engineering software quality in the age of AI. The framework synthesises software-quality models, AI governance guidance, and machine-learning systems research into six interrelated dimensions covering models, data, verification and validation, operations, governance and traceability, and AI-supported quality processes. These dimensions are operationalised through a seven-step assurance method spanning context definition, data assessment, model evaluation, layered validation, monitoring, evidence governance, and validation of internal AI tools. An illustrative retail-banking fraud-monitoring scenario shows how reliability, robustness, interpretability, fairness, drift resilience, and accountability can be assessed within a single workflow. The paper remains conceptual rather than experimental, but it also integrates empirical findings from prior studies to ground key assurance priorities. The contribution is a practical assurance model that translates trustworthy-AI principles into implementable software-engineering practice and identifies a focused agenda for future empirical validation. |
| Keywords: | software quality; AI-enabled systems; trustworthy AI; verification and validation; data quality; monitoring; governance; interpretability; fairness; MLOps |
| Cite Article: | "Engineering Quality in the Age of AI: A Lifecycle Framework for Reliable, Interpretable and Verifiable AI Systems", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b476-b484, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605158.pdf |
| Downloads: | 00081 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605158
Registration ID:211759
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: b476-b484 Country: Prayagraj, UP, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605158 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605158 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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