Software Entropy: A Statistical Mechanics Framework for Software Testing

📅 2026-03-20
📈 Citations: 0
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This work addresses the limitations of existing heuristic-based software testing metrics, which struggle to rigorously quantify a test suite’s ability to constrain the space of valid program implementations. For the first time, statistical mechanics is introduced into software testing: software entropy is formally defined, and a test suite is conceptualized as a macroscopic constraint over the program implementation space. By leveraging mutation analysis to approximate microscopic states, the authors estimate this entropy and propose an information-weighted metric for the distribution of test constraints. This novel measure reveals structural differences among test suites that traditional criteria—such as code coverage—fail to capture. Empirical validation on real-world projects demonstrates the approach’s effectiveness in reducing software entropy, while information weights quantify individual test cases’ contributions to constraining the program space.

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📝 Abstract
The notion of software entropy is often invoked to describe the tendency of software systems to become increasingly disordered as they evolve, yet existing approaches to quantify it are largely heuristic. In this work we introduce a formal definition of software entropy grounded in statistical mechanics, interpreting test suites as executable specifications, that is, as macroscopic constraints on the space of possible program implementations. Within this framework, mutation analysis provides a practical approximation of the locally accessible microstate space, allowing entropy-related quantities to be estimated empirically. We propose metrics that quantify how test suites restrict program space, including an information-weighted measure of the distribution of constraint power across tests. Applying these ideas to a real-world project, we show how test suites reduce software entropy and how information weights reveal structural differences in the contribution of individual tests that traditional metrics such as code coverage fail to capture.
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software entropy
statistical mechanics
test suites
mutation analysis
program space
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software entropy
statistical mechanics
mutation analysis
test suite metrics
information-weighted constraints
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J
Jerónimo Fotinós
Condense Matter Group, Department of Physics, Faculty of Mathematics, Astronomy, Physics, and Computation (FaMAF), National University of Córdoba (UNC), Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.
J
Juan B. Cabral
Grupo de Innovación y Desarrollo Tecnológico, Comisión Nacional de Actividades Espaciales (GVT-CONAE), Ruta Provincial C45, km 8, Falda de Cañete, 5187, Córdoba, Argentina.