Measuring architecture quality by structure plus history analysis

This case study combines known software structure and revision history analysis techniques, in known and new ways, to predict bug-related change frequency, and uncover architecture-related risks in an agile industrial software development project. We applied a suite of structure and history measures and statistically analyzed the correlations between them. We detected architecture issues by identifying outliers in the distributions of measured values and investigating the architectural significance of the associated classes. We used a clustering method to identify sets of files that often change together without being structurally close together, investigating whether architecture issues were among the root causes. The development team confirmed that the identified clusters reflected significant architectural violations, unstable key interfaces, and important undocumented assumptions shared between modules. The combined structure diagrams and history data justified a refactoring proposal that was accepted by the project manager and implemented.