MACs: Mining API code snippets for code reuse

Authors:
Sheng-Kuei Hsu;Shi-Jen Lin
Affiliations:
Department of Information Management, National Central University, Chung-Li 320, Taiwan, ROC;Department of Information Management, National Central University, Chung-Li 320, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

We apply data mining to source code projects to guide developers through related API usage patterns: ''Developers who code the program statement also code...'' Given a set of source code files, the mined association rules suggest related code snippets to form the components of object-oriented programs. The mined sequential rules predict likely additional API sequences within a method. After an initial program statement is given, our MACs prototype can correctly predict useful related API code snippets. In our evaluation, we present two studies investigating the usefulness of MACs in software development tasks. One study evaluated the utility of MACs's association pattern recommendations. The other evaluated usefulness of sequential pattern recommendations, and both drew from a sample of eight source code projects from SourceForge.net. Our experimental evaluation shows that MACs has significant potential to assist developers, especially API newcomers, and provides an alternative method for code reuse.