Optimizing Software Release Management with GPT-Enabled Log Anomaly Detection

As software systems become increasingly complex, detecting anomalies in log data is crucial to ensure high performance and stability. In this paper, we propose a novel approach to log anomaly detection using GPT-3 language models. We utilize the word embedding and tokenizer capabilities of GPT-3 to transform log data into a language model that can identify unusual patterns and anomalies. Our proposed method can be integrated with software release management processes to automatically detect anomalies and improve quality control. By leveraging GPT’s ability to capture complex patterns and relationships within the data, our approach achieved an accuracy of 99.75%, 99.00%, 98.75%, and 99.33% on real-world datasets of Apache, BGL, HDFS, and Thunderbird respectively, outperforming traditional methods of anomaly detection. Our experimental results on real-world log data demonstrate the effectiveness of our proposed approach, making it a promising tool for software release management teams to streamline their processes and ensure the highest level of system performance and reliability.