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This research introduces a groundbreaking approach to software code analysis by marrying game theory with explainable artificial intelligence (XAI) to optimize software solutions. Specifically, we deploy the Nash Equilibrium and Cooperative Game Solutions from game theory as analytical frameworks to scrutinize and optimize interdependencies and interactions within software code. By conceptualizing code components and their interactions as players in a strategic game, our method determines the conditions under which these components can operate in a state of optimal balance-akin to achieving a Nash Equilibrium. This balance ensures that no single component can unilaterally improve its performance without negatively impacting the overall system, mirroring the cooperative stability sought in game-theoretical solutions. Our XAI framework explicates these decisions by mapping out how the Nash Equilibrium and Cooperative Game Solutions inform each recommendation for code optimization. For instance, the approach uses the Nash Equilibrium to demonstrate the stability of certain code configurations, and how deviations from these configurations could lead to suboptimal performance. Similarly, insights from Cooperative Game Solutions highlight potential synergies among code modules, suggesting collaborative adjustments that enhance collective functionality and efficiency. The practical implementation of our system integrates these concepts into a suite of static and dynamic analysis tools, providing a novel layer of intelligence within the typical software development workflow. By introducing game-theoretical reasoning into code analysis, and clearly visualizing these dynamics, developers gain a deeper understanding of the strategic interplay between different parts of their software. This not only aids in optimizing code but also demystifies the AI’s recommendations, fostering greater trust and adoption of AI-driven tools in software development. The benefits of this approach extend beyond improved code performance to include enhanced maintainability and security, fundamentally shifting how developers engage with and enhance their software.

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Integrating Game Theory and Explainable AI for Optimized Software Code Analysis

  • 章节

  • 发表日期：

    2025年

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  • Md. Sarwar Kamal,
  • Wafa Alharbi

摘要

This research introduces a groundbreaking approach to software code analysis by marrying game theory with explainable artificial intelligence (XAI) to optimize software solutions. Specifically, we deploy the Nash Equilibrium and Cooperative Game Solutions from game theory as analytical frameworks to scrutinize and optimize interdependencies and interactions within software code. By conceptualizing code components and their interactions as players in a strategic game, our method determines the conditions under which these components can operate in a state of optimal balance-akin to achieving a Nash Equilibrium. This balance ensures that no single component can unilaterally improve its performance without negatively impacting the overall system, mirroring the cooperative stability sought in game-theoretical solutions. Our XAI framework explicates these decisions by mapping out how the Nash Equilibrium and Cooperative Game Solutions inform each recommendation for code optimization. For instance, the approach uses the Nash Equilibrium to demonstrate the stability of certain code configurations, and how deviations from these configurations could lead to suboptimal performance. Similarly, insights from Cooperative Game Solutions highlight potential synergies among code modules, suggesting collaborative adjustments that enhance collective functionality and efficiency. The practical implementation of our system integrates these concepts into a suite of static and dynamic analysis tools, providing a novel layer of intelligence within the typical software development workflow. By introducing game-theoretical reasoning into code analysis, and clearly visualizing these dynamics, developers gain a deeper understanding of the strategic interplay between different parts of their software. This not only aids in optimizing code but also demystifies the AI’s recommendations, fostering greater trust and adoption of AI-driven tools in software development. The benefits of this approach extend beyond improved code performance to include enhanced maintainability and security, fundamentally shifting how developers engage with and enhance their software.