AI-Augmented Software Quality Engineering: Automated Testing, Defect Prediction, and Reliability Optimization for Cloud-Native Applications
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V7I2P130Keywords:
Artificial Intelligence, Software Quality Engineering, Automated Testing, Defect Prediction, Reliability Optimization, Cloud-Native Applications, Machine Learning, Davos, Continuous Integration, Software ReliabilityAbstract
The rapid adoption of cloud-native architectures has fundamentally transformed software engineering practices by introducing highly distributed, scalable, and dynamically evolving application environments. While cloud-native applications offer flexibility, elasticity, and resilience, they also introduce significant challenges related to software quality assurance, defect management, testing complexity, and system reliability. Traditional software quality engineering approaches often struggle to cope with the velocity and complexity of modern DevOps and continuous deployment ecosystems. In this context, Artificial Intelligence (AI) has emerged as a transformative technology capable of augmenting software quality engineering processes through automated testing, intelligent defect prediction, and reliability optimization mechanisms. This research investigates the integration of AI-driven methodologies within software quality engineering frameworks for cloud-native applications. The study examines machine learning-based automated testing strategies, predictive analytics models for software defect identification, and AI-enabled reliability enhancement techniques designed for distributed cloud environments. A conceptual framework is proposed that integrates automated testing, defect prediction, and reliability optimization into a unified quality engineering lifecycle. The research methodology employs a comprehensive literature analysis and comparative evaluation of contemporary AI-assisted software quality techniques. The findings indicate that AI-driven testing significantly improves test coverage and execution efficiency, while predictive defect analytics reduces maintenance costs and accelerates fault detection. Furthermore, reliability optimization algorithms enhance system availability and service resilience by proactively identifying performance anomalies and infrastructure failures. The study contributes a holistic perspective on AI-augmented software quality engineering and highlights emerging opportunities for intelligent quality assurance in cloud-native software ecosystems.
References
[1] Beller, M., Gousios, G., Panichella, A., & Zaidman, A. (2019). When, how, and why developers test in their IDEs. Empirical Software Engineering, 24(3), 1793–1823.
[2] Sandra, K. (2026). AI-Native and Agentic Data Governance: From Rule-Based Policies to Self-Healing Metadata Systems. International Journal of Emerging Research in Engineering and Technology, 7(2), 46-49. https://doi.org/10.63282/3050-922X.IJERET-V7I2P106
[3] Brahmandam, L. M. K. (2025). A Methodology for Consolidating Decades-Old Enterprise Software Portfolios into a Unified Web Platform: Discovery, Data Model Unification, Architecture, and Migration Approach. American International Journal of Computer Science and Technology, 7(2), 112-121. https://doi.org/10.63282/3117-5481/AIJCST-V7I2P109
[4] Sunkara, R. (2026). Serverless Architecture Patterns for Enterprise AI Agents: ECS Fargate, OpenSearch k-NN, and DynamoDB for Knowledge-Grounded LLM Workflows. International Journal of AI, BigData, Computational and Management Studies, 7(2), 197-201. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P129
[5] Gantikota, S. (2025). JMeter-Driven Performance and Security Validation: A Combined Load Testing and Vulnerability Discovery Methodology for Legacy Java Services. International Journal of Emerging Research in Engineering and Technology, 6(2), 143-147. https://doi.org/10.63282/3050-922X.IJERET-V6I2P117
[6] Paruchuri, J. K. (2026). Agentic Data Engineering: LLM-Augmented Pipeline Generation, Self-Healing ETL, and Autonomous Repair. International Journal of Emerging Research in Engineering and Technology, 7(2), 35-45. https://doi.org/10.63282/3050-922X.IJERET-V7I2P105
[7] Sandra, K. (2024). Data ecosystem modernization ROI: Measurement frameworks and case studies. International Journal of Computer Science Engineering Techniques, 12(6), 1–5.
[8] Arora, A. S., Yachamaneni, T., & Kotadiya, U. (2024). Architectural Optimization of Serverless Big Data Pipelines for AI Workloads Using Cloud Functions and Managed Spark on GCP. International Journal of Emerging Trends in Computer Science and Information Technology, 5(1), 61-68.v
[9] Sunkara, R. (2024). Hardware-in-the-Loop Power Profiling Automation for Consumer Streaming Devices: A Multi-Lab Framework for Regulatory Compliance Validation. International Journal of Emerging Trends in Computer Science and Information Technology, 5(4), 187-191. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I4P121
[10] Paruchuri, J. K. (2022). Survey of Cloud-Native Workflow Orchestration with Apache Airflow.
[11] Chen, T. Y., Kuo, F. C., Merkel, R. G., & Tse, T. H. (2018). Adaptive random testing. IEEE Transactions on Reliability, 67(1), 320–335.
[12] Brahmandam, L. M. K. (2026). A Decision Framework for Multi-Cloud Microservice Deployment across AWS and GCP: Empirical Evaluation of EKS, Cloud Functions, Cloud Run, and Cross-Cloud Networking Patterns. International Journal of Emerging Trends in Computer Science and Information Technology, 7(1), 365-373. https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P152
[13] Seknametla, P. R., & Sunkara, R. (2025). Applying AIOps for Predictive Incident Management in DevOps-Driven Cloud Infrastructure. International Journal, 12(6).
[14] Sandra, K. (2022). Agile Methodologies for Data Engineering Teams: Adoption Patterns and Outcomes.
[15] Paruchuri, J. K. (2025). Natural Language Interfaces for Self-Service Analytics on Data Lakes: Design Patterns, Governance, and Lessons from a Production Deployment. International Journal of Emerging Research in Engineering and Technology, 6(3), 146-151. https://doi.org/10.63282/3050-922X.IJERET-V6I3P118
[16] Kim, S., Whitehead, E., & Zhang, Y. (2019). Classifying software changes: Clean or buggy. IEEE Transactions on Software Engineering, 45(2), 181–196.
[17] Veershetty, G. (2025, June 11). Designing clean-core extension architectures for RISE with SAP using SAP BTP: A reference model and evaluation framework. SSRN. https://doi.org/10.2139/ssrn.6749501
[18] Gantikota, S. (2025). Privacy-By-Design Engineering Under GDPR and CCPA: Practical Patterns for Cross-Border Data Handling In Cloud-Based Applications. International Journal of AI, BigData, Computational and Management Studies, 6(1), 227-231. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P123
[19] Brahmandam, L. M. K. (2024). An Empirical Evaluation of the Medallion Architecture on Databricks and Apache Spark with Snowflake: Throughput, Latency, and Cost for Batch and Real-Time Ingestion Patterns. International Journal of AI, BigData, Computational and Management Studies, 5(3), 197-206. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P122
[20] Seknametla, P. R., & Sunkara, R. . (2024). Threat Modeling Integration in DevSecOps Pipelines: Early-Stage Security Risk Identification Using Shift-Left Approaches. International Journal of Emerging Research in Engineering and Technology, 5(1), 126-133. https://doi.org/10.63282/3050-922X.IJERET-V5I1P115
[21] Paruchuri, J. K. (2021). Exactly-Once Semantics in Distributed Stream Processing at Scale.
[22] Brahmandam, L. M. K. (2025). Design Patterns and Empirical Evaluation of Reusable Terraform Modules Encoding Audit-Ready Defaults for Multi-Account AWS Deployments: A Cross-Team Study across EC2, S3, RDS, EKS, IAM, and Cloud Watch. International Journal of Emerging Research in Engineering and Technology, 6(2), 133-142. https://doi.org/10.63282/3050-922X.IJERET-V6I2P116
[23] Gantikota, S. (2026). Securing Microservice Communication across WCF, JAX-RS, and Spring Boot: Authentication, Authorization, and Audit Patterns for Healthcare Interoperability. American International Journal of Computer Science and Technology, 8(2), 15-20. https://doi.org/10.63282/3117-5481/AIJCST-V8I2P102
[24] Sandra, K. (2022). Scaling Data Engineering Teams: Leadership Models and Organizational Design.
[25] Menzies, T., Greenwald, J., & Frank, A. (2018). Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering, 44(1), 2–13.
[26] Sunkara, R. (2023). Cost-Optimized Energy Compliance Testing for Smart TV Streaming Devices: Achieving Milliwatt-Precision Power Measurement at Sub-One-Thousand-Dollar per Setup. American International Journal of Computer Science and Technology, 5(6), 54-59. https://doi.org/10.63282/3117-5481/AIJCST-V5I6P105
[27] Gantikota, S. (2023). Integrating SonarQube and IBM AppScan into Enterprise CI/CD Pipelines: A Vulnerability Mitigation Framework Achieving Over Eighty Percent Risk Reduction. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 240-244. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P124
[28] Veershetty, G. (2026). Automated Root Cause Analysis in SAP Landscapes Using Large Language Models and Operational Telemetry. International Journal of Emerging Trends in Computer Science and Information Technology, 7(1), 186-191. https://doi.org/10.63282/3050-9246.IJETCSIT-V7I1P127
[29] Paruchuri, J. K. (2021). Lakehouse Architecture: Unifying Data Lakes and Data Warehouses.
[30] Brahmandam, L. M. K. (2024). Performance Engineering for Multi-Tenant Analytic Workloads on Snowflake: An Empirical Study of Clustering, Materialized Views, Query Tuning, and Virtual Warehouse Sizing Across Production Reference Deployments at Billion-Row Scale. International Journal of AI, BigData, Computational and Management Studies, 5(1), 198-207. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P120
[31] Gantikota, S. (2024). Shift-Left Security for Decentralized Engineering Organizations: Embedding SAST, DAST, and Penetration Testing Throughout the Software Development Lifecycle in University and Research Computing Environments. International Journal of Emerging Research in Engineering and Technology, 5(4), 175-179. https://doi.org/10.63282/3050-922X.IJERET-V5I4P118
[32] Sandra, K. (2022). Real-Time Stream Processing with Apache Flink vs Spark Structured Streaming: An Enterprise Comparison.
