Mohanakrishnan Hariharan

Mohanakrishnan Hariharan

Large language models (LLMs) have greatly improved their capability in performing NLP tasks. However, deeper semantic understanding, contextual coherence, and more subtle reasoning are still difficult to obtain. The paper discusses state-of-the-art methodologies that advance LLMs with more advanced NLU techniques, such as semantic parsing, knowledge integration, and contextual reinforcement learning. We analyze the use of structured knowledge graphs, retrieval-augmented generation (RAG), and fine-tuning strategies that match models with human-level understanding. Furthermore, we address the incorporation of transformer-based architectures, contrastive learning, and hybrid symbolic-neural methods that address problems like hallucinations, ambiguity, and inconsistency in the factual perspectives involved in performing complex NLP tasks, such as question-answering text summarization and dialogue generation. Our findings show the importance of semantic precision for enhancing AI-driven language systems and suggest future research directions to bridge the gap between statistical language models and true natural language understanding.

Seshu Babu Barma, Mohanakrishnan Hariharan, Satish Arvapalli

An AI-powered quality engineering platform uses artificial intelligence to boost software quality assessments through automated defect prediction and optimized performance alongside improved feature extraction. Existing models result in difficulties addressing noisy data types together with imbalances, pattern recognition complexities, ineffective feature extraction, and generalization weaknesses. To overcome those existing challenges in this research, we develop a new model Adaptive Differential Evolution based Quantum Variational Autoencoder-Transformer Model (ADE-QVAET), that combines a Quantum Variational Autoencoder-Transformer (QVAET) to obtain high-dimensional latent features and maintain sequential dependencies together with contextual relationships, resulting in superior defect prediction accuracy. Adaptive Differential Evolution (ADE) Optimization utilizes an adaptive parameter tuning method that enhances model convergence and predictive performance. ADE-QVAET integrates advanced AI techniques to create a robust solution for scalable and accurate software defect prediction that represents a top-level AI-driven technology for quality engineering applications. The proposed ADE-QVAET model attains high accuracy, precision, recall, and f1-score during the training percentage (TP) 90 of 98.08%, 92.45%, 94.67%, and 98.12%.

Mohanakrishnan Hariharan

This paper introduces a framework that integrates reinforcement learning (RL) with autonomous agents to enable continuous improvement in the automated process of software test cases authoring from business requirement documents within Quality Engineering (QE) workflows. Conventional systems employing Large Language Models (LLMs) generate test cases from static knowledge bases, which fundamentally limits their capacity to enhance performance over time. Our proposed Reinforcement Infused Agentic RAG (Retrieve, Augment, Generate) framework overcomes this limitation by employing AI agents that learn from QE feedback, assessments, and defect discovery outcomes to automatically improve their test case generation strategies. The system combines specialized agents with a hybrid vector-graph knowledge base that stores and retrieves software testing knowledge. Through advanced RL algorithms, specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), these agents optimize their behavior based on QE-reported test effectiveness, defect detection rates, and workflow metrics. As QEs execute AI-generated test cases and provide feedback, the system learns from this expert guidance to improve future iterations. Experimental validation on enterprise Apple projects yielded substantive improvements: a 2.4% increase in test generation accuracy (from 94.8% to 97.2%), and a 10.8% improvement in defect detection rates. The framework establishes a continuous knowledge refinement loop driven by QE expertise, resulting in progressively superior test case quality that enhances, rather than replaces, human testing capabilities.

Seshu Barma, Mohanakrishnan Hariharan, Satish Arvapalli

An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequential dependencies, resulting in enhanced defect prediction accuracy. ADE optimization enhances model convergence and predictive performance. ADE-QVAET integrates AI-ML techniques such as tuning hyperparameters for scalable and accurate software defect prediction, representing an AI-ML-driven technology for quality engineering. During training with a 90% training percentage, ADE-QVAET achieves high accuracy, precision, recall, and F1-score of 98.08%, 92.45%, 94.67%, and 98.12%, respectively, when compared to the Differential Evolution (DE) ML model.

Mohanakrishnan Hariharan, Satish Arvapalli, Seshu Barma et al.

We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent orchestration, and enhanced contextualization. The system achieves remarkable accuracy improvements from 65% to 94.8% while ensuring comprehensive document traceability throughout the quality engineering lifecycle. Experimental validation of enterprise Corporate Systems Engineering and SAP migration projects demonstrates an 85% reduction in testing timeline, an 85% improvement in test suite efficiency, and projected 35% cost savings, resulting in a 2-month acceleration of go-live.