An AI-Driven Framework for Automated Software Testing Using Natural Language Processing and Deep Learning
Aditya Rajak · 2025
This paper presents an AI-driven software testing framework that combines Natural Language Processing (NLP) and Deep Learning (DL) to transform traditional, rule-based testing into an adaptive and intelligent process. The system is designed to interpret natural language requirements, generate context-aware test cases, and continuously learn from test outcomes to evolve its testing strategy over time. Unlike conventional automation tools that are rigid and break with changes, this framework adapts to evolving software environments—making it resilient, efficient, and self-improving. It mimics the decision-making behavior of experienced testers by learning from historical bug data, requirement shifts, and test results. Evaluated on real-world applications, the framework demonstrated significant improvements in test coverage, defect detection, execution speed, and manual effort reduction. Additionally, qualitative feedback from testers indicated increased satisfaction and productivity. This work aims to reimagine software testing as a collaborative process between human testers and intelligent systems—bringing together automation, contextual understanding, and continuous learning for the next generation of software quality assurance.