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We present Stereocode, a static analysis tool engineered to automatically identify, and re-document software systems written in C++, C#, and/or Java with method and class stereotypes. A stereotype is a simple abstraction that encapsulates the high-level behavior of a method or a class. The tool is built around the srcML infrastructure, an XML representation of source code. Stereocode annotates the srcML input with the computed stereotypes as XML attributes to the function and class tags. We showcase Stereocode’s efficiency in conducting large-scale analysis of software systems, which involves using 1050 repositories from GitHub across C++, C#, and Java. The results provide valuable insights into the distribution of stereotypes. 

An eye-tracking study of 18 developers reading and summarizing Java methods is presented. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. Previous studies on this topic use only short methods presented in isolation usually as images. In contrast, this work presents the study in the Eclipse IDE allowing access to all the source code in the system. The developer can navigate via scrolling and switching files while writing the summary. New eye-tracking infrastructure allows for this improvement in the study environment. Data collected includes eye gazes on source code, written summaries, and time to complete each summary. Unlike prior work that concluded developers focus on the signature the most, these results indicate that they tend to focus on the method body more than the signature. Moreover, both experts and novices tend to revisit control flow terms rather than reading them for a long period. They also spend a significant amount of gaze time and have higher gaze visits when they read call terms. Experts tend to revisit the body of the method significantly more frequently than its signature as the size of the method increases. Moreover, experts tend to write their summaries from source code lines that they read the most.