Software Entropy: A Statistical Mechanics Framework for Software Testing
This work addresses the challenge of measuring software disorder for developers and testers, offering a novel framework that could improve software testing practices, though it is incremental in applying statistical mechanics to an existing heuristic concept.
The authors tackled the problem of quantifying software entropy by introducing a formal definition based on statistical mechanics, using mutation analysis to estimate entropy-related quantities and proposing new metrics that reveal structural differences in test suites, showing that test suites reduce software entropy and that information weights outperform traditional code coverage metrics in capturing test contributions.
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.