Natural Language Processing Technology Based on Artificial Intelligence in Software Testing

The application of Natural Language Processing (NLP) technology based on artificial intelligence technology in the field of software testing is becoming increasingly widespread. This article explores how NLP technology based on BERT (Bidirectional Encoder Representations from Transformers) can be deeply integrated into various aspects of software testing, achieving automation in requirement analysis, test case generation, defect report processing, and test result description. Through a series of experiments, the significant advantages of BERT technology in improving testing efficiency, accuracy, and reducing manual intervention have been verified. This article first introduces the theoretical foundation and key technologies of NLP technology in software testing, then describes in detail the integration framework based on BERT algorithm, and demonstrates its application effects in test data preparation, preprocessing, automated defect recognition, and report generation through experimental results. Experimental data shows that BERT technology has significant advantages in test case coverage, defect detection accuracy, and test regression cycle, with a maximum regression cycle of only 599ms. Despite challenges such as model generalization ability, data quality dependency, and computational resource consumption, the integrated framework of BERT technology provides new possibilities for the automation and intelligence of software testing.