{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/2073"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/2073","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Augmenting large language models with static code analysis for accelerated software development and quality improvements","abstract":"This thesis presents the Next-Generation Quality Accelerator (NGQA), an automated end-to-end pipeline driven by large language models (LLMs) to accelerate the quality assurance (QA) phase of the software development lifecycle using static analysis tools. NGQA integrates detection, grounding, revision, validation, and coordination into a unified workflow through a verification-aware artificial intelligence (AI) agent architecture. The pipeline consists of six steps that combine SonarQube for static issue detection, retrieval-augmented generation (RAG) based false positive mitigation, LLM-based code revision, structural dependency analysis, test case generation using Local Chain-of-Thought (LCoT) reasoning, and multi-metric quality validation. It supports both cloud-based and fully local LLM deployment. Empirical evaluation on 70 repositories across seven programming languages shows that NGQA resolves 83.5% of issues, improves PassRatio by 16.5%, CodeBLEU by 28.8%, CodeScore by 24.0%, and achieves 89% F1-score in false positive mitigation compared to baseline methods. NGQA achieves a 32.6-fold acceleration over manual QA, completing the evaluation in 95.9 hours compared to an estimated 3,126 person-hours manually.","abstract_html":"This thesis presents the Next-Generation Quality Accelerator (NGQA), an automated end-to-end pipeline driven by large language models (LLMs) to accelerate the quality assurance (QA) phase of the software development lifecycle using static analysis tools. NGQA integrates detection, grounding, revision, validation, and coordination into a unified workflow through a verification-aware artificial intelligence (AI) agent architecture. The pipeline consists of six steps that combine SonarQube for static issue detection, retrieval-augmented generation (RAG) based false positive mitigation, LLM-based code revision, structural dependency analysis, test case generation using Local Chain-of-Thought (LCoT) reasoning, and multi-metric quality validation. It supports both cloud-based and fully local LLM deployment. Empirical evaluation on 70 repositories across seven programming languages shows that NGQA resolves 83.5% of issues, improves PassRatio by 16.5%, CodeBLEU by 28.8%, CodeScore by 24.0%, and achieves 89% F1-score in false positive mitigation compared to baseline methods. NGQA achieves a 32.6-fold acceleration over manual QA, completing the evaluation in 95.9 hours compared to an estimated 3,126 person-hours manually.","abstract_has_math":false,"creators":["Abtahi, Seyed Moein"],"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":["Azim, Akramul"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-02-01","date_published":"2026-02-01","updated_at":"2026-07-24T05:35:32Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/2073","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Azim, Akramul"]},{"key":"dc:creator","label":"Author","values":["Abtahi, Seyed Moein"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-03-26T17:15:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-02-01"]},{"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/10155/2073"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents the Next-Generation Quality Accelerator (NGQA), an automated end-to-end pipeline driven by large language models (LLMs) to accelerate the quality assurance (QA) phase of the software development lifecycle using static analysis tools. 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NGQA achieves a 32.6-fold acceleration over manual QA, completing the evaluation in 95.9 hours compared to an estimated 3,126 person-hours manually."]},{"key":"dc:title","label":"Title","values":["Augmenting large language models with static code analysis for accelerated software development and quality improvements"]}]}],"canonical_facts":{"dc:contributor.advisor":["Azim, Akramul"],"dc:creator":["Abtahi, Seyed Moein"],"dc:date.accessioned":["2026-03-26T17:15:39Z"],"dc:date.issued":["2026-02-01"],"dc:description.abstract":["This thesis presents the Next-Generation Quality Accelerator (NGQA), an automated end-to-end pipeline driven by large language models (LLMs) to accelerate the quality assurance (QA) phase of the software development lifecycle using static analysis tools. NGQA integrates detection, grounding, revision, validation, and coordination into a unified workflow through a verification-aware artificial intelligence (AI) agent architecture. The pipeline consists of six steps that combine SonarQube for static issue detection, retrieval-augmented generation (RAG) based false positive mitigation, LLM-based code revision, structural dependency analysis, test case generation using Local Chain-of-Thought (LCoT) reasoning, and multi-metric quality validation. It supports both cloud-based and fully local LLM deployment. Empirical evaluation on 70 repositories across seven programming languages shows that NGQA resolves 83.5% of issues, improves PassRatio by 16.5%, CodeBLEU by 28.8%, CodeScore by 24.0%, and achieves 89% F1-score in false positive mitigation compared to baseline methods. 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