Stereocode: A Tool for Automatic Identification of Method and Class Stereotypes for Software Systems

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. A demo video is available at: https://youtu.be/D90xwUIPbOI.

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Publication Details

Published
2024-10-06
DOI
https://doi.org/10.1109/icsme58944.2024.00099
Primary Topic
Software Engineering Research
Type
article
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article

Stereocode: A Tool for Automatic Identification of Method and Class Stereotypes for Software Systems

Joshua A.C. Behler, Jonathan I. Maletic, Natalia Dragan, Michael L. Collard et al.
Software Engineering Research
article

Stereocode: A Tool for Automatic Identification of Method and Class Stereotypes for Software Systems

Joshua A.C. Behler, Jonathan I. Maletic, Natalia Dragan, Michael L. Collard, Ali F. Al-Ramadan, Michael J. Decker
article en

Abstract

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. A demo video is available at: https://youtu.be/D90xwUIPbOI.

University of Akron (US), Kent State University (US), Bowling Green State University (US)
Openalex Percentile: Top 63%
Software Engineering Research
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Stereocode: A Tool for Automatic Identification of Method and Class Stereotypes for Software Systems — Joshua A.C. Behler, Jonathan I. Maletic, et al. · (2024) | TGRS Research Map | TGRS