AI-Powered AOP: Enhancing Runtime Monitoring with Large Language Models and Statistical Learning

Modern software systems must adapt to dynamic artificial intelligence (AI) environments and evolving requirements. Aspect-oriented programming (AOP) effectively isolates crosscutting concerns (CCs) into single modules called aspects, enhancing quality metrics, and simplifying testing. However, AOP i...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2024-01, Vol.15 (11)
Main Authors: AlSobeh, Anas, Shatnawi, Amani, Al-Ahmad, Bilal, Aljmal, Alhan, Khamaiseh, Samer
Format: Article
Language:English
Subjects:
Artificial intelligence
Behavior
Computer programming
Computer science
Explainable artificial intelligence
Java
Language
Large language models
Modularity
Monitoring
Software
Software development
Statistical models
Weaving
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Summary:Modern software systems must adapt to dynamic artificial intelligence (AI) environments and evolving requirements. Aspect-oriented programming (AOP) effectively isolates crosscutting concerns (CCs) into single modules called aspects, enhancing quality metrics, and simplifying testing. However, AOP implementation can lead to unexpected program outputs and behavior changes. This paper proposes an AI-enhanced, adaptive monitoring framework for validating program behaviors during aspect weaving that integrates AOP interfaces (AOPIs) with large language models (LLMs), i.e. GPT-Codex AI, to dynamically generate and optimize monitoring aspects and statistical models in realtime. This enables intelligent run-time analysis, adaptive model checking, and natural language (NL) interaction. We tested the framework on ten diverse Java classes from JHotdraw 7.6 by extracting context and numerical data and building a dataset for analysis. By dynamically refining aspects and models based on observed behavior, its results showed that the framework maintained the integrity of the Java OOP class while providing predictive insights into potential conflicts and optimizations. Results demonstrate the framework’s efficacy in detecting subtle behavioral changes induced by aspect weaving, with a 94% accuracy in identifying potential conflicts and a 37% reduction in false positives compared to traditional static analysis techniques. Furthermore, the integration of explainable AI provides developers with clear, actionable explanations for flagged behaviors through NL interfaces, enhancing interpretability and trust in the system.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0151113