{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/83505"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/83505","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Modeling Software Developer Expertise and Inexpertise to Handle Diverse Information Needs","abstract":"Expert software developer recommendation is a mature research field with many different techniques being developed to help automate the search for experts to help with development tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don't know as well. Previous research on developer expertise hasn't taken inexpertise into account. Another restriction is that all previous expert developer recommendation research has focused on recommending developers for a single development task or expertise need, such as fixing a bug report or helping with a change request. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. We find that inexpertise exists, can be measured, and that it can be used to direct inspection effort to find potentially incorrect or buggy commits. Additionally we investigate how different expertise finding techniques perform on a diverse set of long and short expertise queries and develop new techniques that can get more consistent cross query performance.","abstract_html":"Expert software developer recommendation is a mature research field with many different techniques being developed to help automate the search for experts to help with development tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don't know as well. Previous research on developer expertise hasn't taken inexpertise into account. Another restriction is that all previous expert developer recommendation research has focused on recommending developers for a single development task or expertise need, such as fixing a bug report or helping with a change request. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. We find that inexpertise exists, can be measured, and that it can be used to direct inspection effort to find potentially incorrect or buggy commits. Additionally we investigate how different expertise finding techniques perform on a diverse set of long and short expertise queries and develop new techniques that can get more consistent cross query performance.","abstract_has_math":false,"creators":["Claytor, Frank L."],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Computer Science and Applications","degree_department":"Computer Science","school":null,"contributors":[],"advisors":[],"committee_chairs":["Servant Cortes, Francisco Javier"],"committee_members":["Edwards, Stephen H.","Huang, Bert"],"year":2018,"date_issued":"2018-06-08","date_published":"2018-06-08","updated_at":"2026-07-22T22:19:37Z","subjects":["Expertise","Expert Recommendation","Software Engineering"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:15305"],"render_values":[{"text":"vt_gsexam:15305","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/83505","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Servant Cortes, Francisco Javier"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Edwards, Stephen H.","Huang, Bert"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science"]},{"key":"dc:creator","label":"Author","values":["Claytor, Frank L."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-06-09T08:01:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-06-09T08:01:21Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-06-08"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science and Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Expertise","Expert Recommendation","Software Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:15305"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/83505"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Expert software developer recommendation is a mature research field with many different techniques being developed to help automate the search for experts to help with development tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don't know as well. Previous research on developer expertise hasn't taken inexpertise into account. Another restriction is that all previous expert developer recommendation research has focused on recommending developers for a single development task or expertise need, such as fixing a bug report or helping with a change request. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. We find that inexpertise exists, can be measured, and that it can be used to direct inspection effort to find potentially incorrect or buggy commits. Additionally we investigate how different expertise finding techniques perform on a diverse set of long and short expertise queries and develop new techniques that can get more consistent cross query performance."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Expert software developers are a useful source of information. There have been many papers that research techniques for recommending expert developers for different tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don’t know as well. Another restriction is that all previous work on recommending expert developers has focused on recommending developers for a single development task or question. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. In our first chapter we show that inexpertise exists, can be measured, and that it can be used to help identify potentially buggy or incorrect code. In the second chapter we investigate how different techniques for finding expert developers perform when evaluated on different kinds of expertise finding tasks to find which technique works well on multiples types of tasks."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Modeling Software Developer Expertise and Inexpertise to Handle Diverse Information Needs"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Servant Cortes, Francisco Javier"],"dc:contributor.committeemember":["Edwards, Stephen H.","Huang, Bert"],"dc:contributor.department":["Computer Science"],"dc:creator":["Claytor, Frank L."],"dc:date.accessioned":["2018-06-09T08:01:21Z"],"dc:date.available":["2018-06-09T08:01:21Z"],"dc:date.issued":["2018-06-08"],"dc:description.abstract":["Expert software developer recommendation is a mature research field with many different techniques being developed to help automate the search for experts to help with development tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don't know as well. Previous research on developer expertise hasn't taken inexpertise into account. Another restriction is that all previous expert developer recommendation research has focused on recommending developers for a single development task or expertise need, such as fixing a bug report or helping with a change request. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. We find that inexpertise exists, can be measured, and that it can be used to direct inspection effort to find potentially incorrect or buggy commits. Additionally we investigate how different expertise finding techniques perform on a diverse set of long and short expertise queries and develop new techniques that can get more consistent cross query performance."],"dc:description.abstractgeneral":["Expert software developers are a useful source of information. There have been many papers that research techniques for recommending expert developers for different tasks and questions. But all previous research on recommending expert developers has had two constant restrictions. First, all previous expert recommendation work assumed that developers only demonstrate positive expertise. But developers can also make mistakes and demonstrate negative expertise, referred to as inexpertise, and show which concepts they don’t know as well. Another restriction is that all previous work on recommending expert developers has focused on recommending developers for a single development task or question. But not all expertise needs can be easily classified into one of these groups, and having different techniques for every possible task type would be difficult and confusing to maintain and use. In our first chapter we show that inexpertise exists, can be measured, and that it can be used to help identify potentially buggy or incorrect code. In the second chapter we investigate how different techniques for finding expert developers perform when evaluated on different kinds of expertise finding tasks to find which technique works well on multiples types of tasks."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:15305"],"dc:identifier.uri":["http://hdl.handle.net/10919/83505"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Expertise","Expert Recommendation","Software Engineering"],"dc:title":["Modeling Software Developer Expertise and Inexpertise to Handle Diverse Information Needs"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science and Applications"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:37Z"}