AI-Driven Intelligent Quality Engineering Architecture
This research proposes an AI-driven quality engineering architecture designed to improve software reliability through predictive testing, machine learning analytics, and intelligent automation systems.
Traditional software testing approaches rely heavily on manual test creation and static automation frameworks. The proposed architecture introduces intelligent systems capable of predicting defect-prone software modules and automatically generating targeted testing strategies.
Architecture Overview
The architecture integrates repository analytics, machine learning models, automated test generation frameworks, and continuous testing pipelines into a unified intelligent quality engineering platform.
Key Components
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Repository Analytics Engine
Analyzes software repositories, commit history, and development patterns to identify defect-prone modules. -
Defect Prediction Model
Machine learning models trained on historical software data to predict potential defects in future releases. -
Autonomous Test Generation Engine
Automatically generates intelligent test cases targeting high-risk software components. -
Automated Test Execution Platform
Executes generated tests across distributed testing environments including web, mobile, and API systems. -
AI Quality Analytics Dashboard
Provides predictive insights and quality metrics to development teams.
Benefits for Software Organizations
- Earlier identification of defect-prone modules
- Reduced testing effort through autonomous test generation
- Improved software reliability and quality
- Faster software release cycles
- AI-driven decision making for software testing
Future Research Directions
Future research will explore integration of large language models for automated test design, adaptive testing systems capable of learning from production incidents, and AI-driven testing strategies for large-scale distributed systems.