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In large-scale software systems, the majority of change-prone files are usually architecturally connected, and their architectural connections often exhibit design flaws, which propagate change-proneness among files and increase maintenance costs. Complementary to the identification and definition of the files involved architecture design flaws, the automatic detection at the method level is important for developers to understand fine-grained contributors to the architecture flaws that have incurred high maintenance costs. In this paper, we propose an approach to identify method-level hotspots. Each hotspot contains a group of evolutionarily connected methods or attributes that participate in architecture design flaws and continuously accumulate maintenance difficulties. Our investigations on six large-scale projects show that the attributes or methods captured in method-level hotspots are more change-prone than the others. The results also show that the growth trend of the maintenance effort spent on each method-level hotspot could be modeled and monitored by our approach, which sheds light in the decision of design refactoring.

Detecting and Modeling Method-level Hotspots in Architecture Design Flaws