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In this paper, we explore the feasibility of finding algorithmimplementations from code. Successfully matching code and algorithms can helpunderstand unknown code, provide reference implementations, and automaticallycollect data for learning-based program synthesis. To achieve the goal, wedesigned a new language named p-language to specify the algorithms and a staticanalyzer for the p-language to automatically extract control flow, math, andnatural language information from the algorithm descriptions. We embedded theoutput of p-language (p-code) and source code in a common vector space usingself-supervised machine learning methods to match algorithm with code withoutany manual annotation. We developed a tool named Beryllium. It takes pseudocode as a query and returns a list of ranked code snippets that likely matchthe algorithm query. Our evaluation on Stony Brook Algorithm Repository andpopular GitHub projects show that Beryllium significantly outperformed thestate-of-the-art code search tools in both C and Java. Specifically, for 98.5%,93.8%, and 66.2% queries, we found the algorithm implementations in the top 25,10, and 1 ranked list, respectively. Given 87 algorithm queries, we foundimplementations for 74 algorithms in the GitHub projects where we did not knowthe algorithms before.

Beryllium: Neural Search for Algorithm Implementations