• C Meena
• Dr. T. Sree Lakshmi

Modern software systems continuously generate extensive execution logs and runtime data, reflecting their internal processes, resource usage, and external interactions. Accurate prediction of software behavior is crucial for maintaining system reliability, reducing downtime, and enabling proactive issue resolution. This paper presents a novel framework for predictive modeling using Long Short-Term Memory (LSTM) networks—a class of recurrent neural networks capable of capturing long-term dependencies in sequential data. Our system processes structured logs and performance metrics to forecast key indicators such as CPU utilization, memory consumption, and abnormal events. The LSTM model is trained on historical traces from multiple software applications and validated across different environments to ensure generalizability. Compared to traditional time series and rule-based techniques, our model demonstrates improved accuracy, scalability, and robustness. This work contributes a practical, AI-powered solution for real-time software monitoring, suitable for integration into DevOps workflows and enterprise monitoring systems.Our approach focuses on analyzing historical logs and runtime metrics such as CPU load, memory consumption, and input/output operations to predict future behavior. This enables early detection of anomalies, performance degradation, or failures, thus enhancing operational resilience.The adaptability of LSTM across various types of software workloads further validates its applicability in modern DevOps environments. This work provides a foundation for intelligent monitoring systems that not only react to issues but anticipate them, ensuring continuity and optimized performance.

Software Behavior Prediction, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), System Logs Analysis, Anomaly Detection, Time-Series Forecasting, Deep Learning in DevOps, Predictive Maintenance.

"Intelligent Software Monitoring: A Deep Learning Approach with LSTM for Behavior Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.g328-g332, July-2025, Available :http://www.jetir.org/papers/JETIR2507648.pdf

"Intelligent Software Monitoring: A Deep Learning Approach with LSTM for Behavior Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppg328-g332, July-2025, Available at : http://www.jetir.org/papers/JETIR2507648.pdf