Program comprehension is an essential activity to perform software maintenance and evolution. Comprehensibility often encompasses the analysis of individual logical units, called features, which are often scattered through many program modules. Understanding how the feature code is implemented along the software evolution history is essential, for instance, to perform refactoring activities. However, existing tools do not provide means to comprehend the feature code evolution. To overcome this shortcoming, this paper presents a tool called Source Miner Evolution (SME) that provides multiple interactive and coordinated views to comprehend feature code evolution. SME implements a feature-sensitive comparison of multiple program versions. Our usability assessment with experienced developers indicated that SME allows them to efficiently perform recurring comprehension tasks on evolving feature code. The developers' performance was influenced by the combination of visual SME mechanisms, such as colors, tool tips and menu-popup interactions over the features' code elements.

Program comprehension is a key activity through maintenance and evolution of large-scale software systems. The understanding of a program often requires the evolution analysis of individual functionalities, so-called features. The comprehension of evolving features is not trivial as their implementations are often tangled and scattered through many modules. Even worse, existing techniques are limited in providing developers with direct means for visualizing the evolution of features' code. This work presents a proactive and interactive visualization strategy to enable feature evolution analysis. It proactively identifies code elements of evolving features and provides multiple views to present their structure under different perspectives. The novel visualization strategy was compared to a lightweight visualization strategy based on a tree-structure. We ran a controlled experiment with industry developers, who performed feature evolution comprehension tasks on an industrial-strength software. The results showed that the use of the proposed strategy presented significant gains in terms of correctness and execution time for feature evolution comprehension tasks.