{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2109"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2109","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Toward automated requirements engineering: empirical and architectural foundations for structured parsing and knowledge discovery","abstract":"Requirements Engineering (RE) relies heavily on natural language, which is often vague, inconsistently structured, and difficult to automate reliably. This thesis presents TRAC-RE, a Traceable, Reliable, Auditable, and Contextual framework for automated requirements engineering. The framework consists of four interconnected phases addressing requirement identification, audit-grade keyword extraction, structured large language model (LLM) parsing, and implicit keyword discovery. A context-aware requirement identification model improved F1 from 0.664 to 0.894 on 110 real-world FinTech and SaaS documents. An audit-grade keyword extraction pipeline achieved perfect precision while revealing a structural coverage ceiling of 1.5 canonical keywords per artifact. A governed High-Level JSON (HLJ) parsing pipeline improved tag precision from 0.657 to 0.897. Finally, a multi-signal implicit discovery engine grounded in a 13,725-entry domain dictionary addressed limitations of explicit extraction. Together, these contributions establish empirical and architectural foundations for reliable, structured, and auditable automated requirements engineering.","abstract_html":"Requirements Engineering (RE) relies heavily on natural language, which is often vague, inconsistently structured, and difficult to automate reliably. This thesis presents TRAC-RE, a Traceable, Reliable, Auditable, and Contextual framework for automated requirements engineering. The framework consists of four interconnected phases addressing requirement identification, audit-grade keyword extraction, structured large language model (LLM) parsing, and implicit keyword discovery. A context-aware requirement identification model improved F1 from 0.664 to 0.894 on 110 real-world FinTech and SaaS documents. An audit-grade keyword extraction pipeline achieved perfect precision while revealing a structural coverage ceiling of 1.5 canonical keywords per artifact. A governed High-Level JSON (HLJ) parsing pipeline improved tag precision from 0.657 to 0.897. Finally, a multi-signal implicit discovery engine grounded in a 13,725-entry domain dictionary addressed limitations of explicit extraction. Together, these contributions establish empirical and architectural foundations for reliable, structured, and auditable automated requirements engineering.","abstract_has_math":false,"creators":["Patel, Dvip"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Software Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Alwidian, Sanaa"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01","date_published":"2026-05-01","updated_at":"2026-07-24T05:35:28Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2109","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Alwidian, Sanaa"]},{"key":"dc:creator","label":"Author","values":["Patel, Dvip"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-06-05T17:12:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-05-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Software Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"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.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/2109"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Requirements Engineering (RE) relies heavily on natural language, which is often vague, inconsistently structured, and difficult to automate reliably. This thesis presents TRAC-RE, a Traceable, Reliable, Auditable, and Contextual framework for automated requirements engineering. The framework consists of four interconnected phases addressing requirement identification, audit-grade keyword extraction, structured large language model (LLM) parsing, and implicit keyword discovery. A context-aware requirement identification model improved F1 from 0.664 to 0.894 on 110 real-world FinTech and SaaS documents. An audit-grade keyword extraction pipeline achieved perfect precision while revealing a structural coverage ceiling of 1.5 canonical keywords per artifact. A governed High-Level JSON (HLJ) parsing pipeline improved tag precision from 0.657 to 0.897. Finally, a multi-signal implicit discovery engine grounded in a 13,725-entry domain dictionary addressed limitations of explicit extraction. 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