Identifying Code Changes for Architecture Decay via a Metric Forest Structure

During long-term software evolution, it is inevitable that an accumulation of changes leads to architectural erosion and debt. Diverse metric-based methods have been developed to identify architectural problems that violate design principles and degrade software maintainability. However, there still exists a gap between the implementation-level metrics and architecture-level metrics. Consequently, it hinders the comprehensibility, interpretability, and indicative(-bility) of the measurement results. To fill this gap, we propose the dbMIT to identify potential code changes that make architecture decay. Our dbMIT first integrates popular metrics such as the CK suite. Then dbMIT constructs a forest structure of metrics, serving as a knowledge base to relate the metrics across levels together. The forest aims to link the architecture-level metrics and implementation-level metrics. Via pre-defined rules using the forest structure, our dbMIT identifies code changes that potentially cause the architecture decay. The usage of forest structure of metrics makes it easy for developers to understand detection results, explain why the detected code changes are potential contributors to the decay, and indicate the code scope for resolution. We also contribute a web-based tool to integrate our dbMIT. Our experiments on the collected projects demonstrate the effectiveness of dbMIT against a history-based ground-truth.