Choosing an algorithm to use can depend on a variety of factors such as runtime, space, and
problem requirements. Many algorithms already have tested implementations in open source code.
Reusing or interchanging algorithms can help save development time and improve the performance
of applications.
Existing code search techniques often rely heavily on natural language components of the code.
Simple techniques, such as Grep, are sensitive to the naming choices and conventions in code. Grep
in particular do not precisely find implementations, outputting single lines. Grep does not rank
the result, and is subject to lots of noise.
We develop a technique to search for algorithms in code using existing pseudo code as a query.
We leverage the structural, mathematical and natural language components of pseudo code to find
its corresponding implementation in code. This approach defines a simple language to represent
pseudo code with atoms that include different features of the algorithm. We then use these features
to search code using a bounding box and extract the code snippet that contains the functionality
of the pseudo code.
We collected 19 different repositories in both C and Java and searched for 27 different algorithms.
Using our technique we found over 60 algorithm implementations in roughly 1.8 million lines of
code. We also conduct a comparison of our tool against a search implementation using a popular
enterprise search platform Apache Solr and show our approach can find more algorithms with high
rank.
","abstract_html":"<p>Choosing an algorithm to use can depend on a variety of factors such as runtime, space, and</p> <p>problem requirements. Many algorithms already have tested implementations in open source code.</p> <p>Reusing or interchanging algorithms can help save development time and improve the performance</p> <p>of applications.</p> <p>Existing code search techniques often rely heavily on natural language components of the code.</p> <p>Simple techniques, such as Grep, are sensitive to the naming choices and conventions in code. Grep</p> <p>in particular do not precisely find implementations, outputting single lines. Grep does not rank</p> <p>the result, and is subject to lots of noise.</p> <p>We develop a technique to search for algorithms in code using existing pseudo code as a query.</p> <p>We leverage the structural, mathematical and natural language components of pseudo code to find</p> <p>its corresponding implementation in code. This approach defines a simple language to represent</p> <p>pseudo code with atoms that include different features of the algorithm. We then use these features</p> <p>to search code using a bounding box and extract the code snippet that contains the functionality</p> <p>of the pseudo code.</p> <p>We collected 19 different repositories in both C and Java and searched for 27 different algorithms.</p> <p>Using our technique we found over 60 algorithm implementations in roughly 1.8 million lines of</p> <p>code. We also conduct a comparison of our tool against a search implementation using a popular</p> <p>enterprise search platform Apache Solr and show our approach can find more algorithms with high</p> <p>rank.</p>","abstract_has_math":false,"creators":["Leslie, Stroh"],"institution":null,"degree_name":"Master of Science","degree_level":"thesis","degree_discipline":"Computer Science","degree_department":"Department of Computer Science","school":null,"contributors":[],"advisors":["Wei . Le"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-01-01","date_published":"2019-01-01","updated_at":"2026-07-24T02:38:12Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/etd/17728/"],"render_values":[{"text":"archive/lib.dr.iastate.edu/etd/17728/","href":null,"code":true}]}]},"links":{"outbound_url":"https://dr.lib.iastate.edu/handle/20.500.12876/31911","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Wei . Le"]},{"key":"dc:contributor.department","label":"Department","values":["Department of Computer Science"]},{"key":"dc:creator","label":"Author","values":["Leslie, Stroh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-02-12T22:57:28.000"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-06-30T03:20:30Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-06-30T03:20:30Z"]},{"key":"dc:date.issued","label":"Date","values":["2019-01-01"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/etd/17728/"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://dr.lib.iastate.edu/handle/20.500.12876/31911"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Choosing an algorithm to use can depend on a variety of factors such as runtime, space, and
problem requirements. Many algorithms already have tested implementations in open source code.
Reusing or interchanging algorithms can help save development time and improve the performance
of applications.
Existing code search techniques often rely heavily on natural language components of the code.
Simple techniques, such as Grep, are sensitive to the naming choices and conventions in code. Grep
in particular do not precisely find implementations, outputting single lines. Grep does not rank
the result, and is subject to lots of noise.
We develop a technique to search for algorithms in code using existing pseudo code as a query.
We leverage the structural, mathematical and natural language components of pseudo code to find
its corresponding implementation in code. This approach defines a simple language to represent
pseudo code with atoms that include different features of the algorithm. We then use these features
to search code using a bounding box and extract the code snippet that contains the functionality
of the pseudo code.
We collected 19 different repositories in both C and Java and searched for 27 different algorithms.
Using our technique we found over 60 algorithm implementations in roughly 1.8 million lines of
code. We also conduct a comparison of our tool against a search implementation using a popular
enterprise search platform Apache Solr and show our approach can find more algorithms with high
rank.
"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Identify algorithms from code"]}]}],"canonical_facts":{"dc:contributor.advisor":["Wei . Le"],"dc:contributor.department":["Department of Computer Science"],"dc:creator":["Leslie, Stroh"],"dc:date":["2020-02-12T22:57:28.000"],"dc:date.accessioned":["2020-06-30T03:20:30Z"],"dc:date.available":["2020-06-30T03:20:30Z"],"dc:date.issued":["2019-01-01"],"dc:description.abstract":["Choosing an algorithm to use can depend on a variety of factors such as runtime, space, and
problem requirements. Many algorithms already have tested implementations in open source code.
Reusing or interchanging algorithms can help save development time and improve the performance
of applications.
Existing code search techniques often rely heavily on natural language components of the code.
Simple techniques, such as Grep, are sensitive to the naming choices and conventions in code. Grep
in particular do not precisely find implementations, outputting single lines. Grep does not rank
the result, and is subject to lots of noise.
We develop a technique to search for algorithms in code using existing pseudo code as a query.
We leverage the structural, mathematical and natural language components of pseudo code to find
its corresponding implementation in code. This approach defines a simple language to represent
pseudo code with atoms that include different features of the algorithm. We then use these features
to search code using a bounding box and extract the code snippet that contains the functionality
of the pseudo code.
We collected 19 different repositories in both C and Java and searched for 27 different algorithms.
Using our technique we found over 60 algorithm implementations in roughly 1.8 million lines of
code. We also conduct a comparison of our tool against a search implementation using a popular
enterprise search platform Apache Solr and show our approach can find more algorithms with high
rank.
"],"dc:format.mimetype":["application/pdf"],"dc:identifier":["archive/lib.dr.iastate.edu/etd/17728/"],"dc:identifier.uri":["https://dr.lib.iastate.edu/handle/20.500.12876/31911"],"dc:language.iso":["en"],"dc:title":["Identify algorithms from code"],"dc:type":["thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T02:38:12Z"}