International Journal of Computational and Electronics Aspects in Engineering
Volume 7 · Issue 1 · March 2026 · pp. 82-90
Research Article · Peer Reviewed
Received: December 20, 2026 · Accepted: February 20, 2026 · Published: March 04, 2026
DOI: 10.26706/ijceae.7.1.20260109
Open Access · CC BY 4.0

Intent-Aware Software Performance Optimization Based on Application Behavioral Learning

Hasan Fadil Qasim*
College of Agriculture, University of Misan, Misan, Iraq.

*Corresponding author: hasan.fadhil@uomisan.edu.iq
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Abstract

Contemporary software systems operate under varying performance tendencies and usage purposes, where identical resource conditions may require entirely different optimization strategies. Traditional software performance optimization approaches rely mainly on low-level system metrics and often ignore application intent, resulting in inefficient or even conflicting optimization decisions. This paper proposes an intent-aware software performance optimization framework that integrates application behavioral profiling as a decision layer. The framework analyzes runtime behavior patterns to automatically classify execution intent (e.g., interactive, batch-oriented, or real-time processing) and dynamically applies intent-specific optimization policies based on the identified intent type. Machine learning techniques are employed to learn behavioral traits from performance data, enabling adaptive optimization based on inferred intent rather than raw metrics alone. The framework is implemented and evaluated using MATLAB, demonstrating improved responsiveness for interactive workloads and enhanced throughput for batch-oriented applications compared to conventional metric-based optimization approaches. The results confirm that incorporating application intent significantly enhances adaptability and effectiveness in software performance engineering.

Keywords

Intent-Aware Software Systems Software Optimization Behavioral Profiling Machine Learning Application Intent Classification Runtime Optimization Performance Engineering Adaptive Systems

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