[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-246134-105":53,"doc-detail-246134-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","mining-architectural-knowledge-in-issue-tracking-systems-thesis-abstract","Mining Architectural Knowledge in Issue Tracking Systems - Thesis Abstract","","Architectural knowledge extraction and access remain challenging because design intent is dispersed across developer discussions and project artifacts. This thesis evaluates two specialized tools for mining architectural knowledge from issue tracking systems, where software developers coordinate and debate design. Extracted issues are annotated with architectural design decisions using a developed coding book. Comparative analysis examines how architectural knowledge characteristics and issue properties differ between the tools and supports measuring effectiveness through multiple information-retrieval metrics and issue-level distributions.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":11,"@type":70,"position":76},"https://docshare.wps.com/template/presentations/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/mining-architectural-knowledge-in-issue-tracking-systems-thesis-abstract/246134/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/mining-architectural-knowledge-in-issue-tracking-systems-thesis-abstract/246134.png","ImageObject",442,249,{"name":88,"@type":89},"Sophia Brooks","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-19","2026-09-12",true,{"@type":98,"interactionType":99,"userInteractionCount":15},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What problem does the thesis address?","Question",{"text":108,"@type":109},"Recording and accessing architectural knowledge in a systematic way is difficult, and valuable design decisions can be lost over time. The thesis focuses on extracting such knowledge from issue tracking systems.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How is architectural knowledge extracted and prepared for analysis?",{"text":113,"@type":109},"Two tools for mining architectural knowledge from issues are evaluated, and extracted issues are annotated with architectural design decisions using a coding book developed for the annotation process.",{"name":115,"@type":106,"acceptedAnswer":116},"How are the tools compared in terms of effectiveness?",{"text":117,"@type":109},"Effectiveness is measured using information-retrieval metrics such as precision and NDCG, along with additional analyses like missed parents and issue property distributions.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},246134,1791731043,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":15,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":37,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":20},962084925636,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Mining architectural knowledge in issue tracking  \nsystems  \nSaid Faroghi  \nSupervisors: Dr. Mohamed Soliman, Prof. Paris Avgeriou  \n03 02 2022  \nAbstract  \nRecording and accessing architectural knowledge (AK) is not a trivial task. A promising source of AK could reside in issue tracking systems, which are platforms for developers to coordinate building software, and therefore it is a hotspot for software-related discussions. We have evaluated two tools that specialize in extracting AK issues, by annotating the generated issues with architectural design decisions (ADD) . We developed a coding book to help the annotation process. Furthermore, we analyzed the issues to see how their AK characteristics and other properties di􀀋er based on the tools they  \ncame from.  \nContents  \n1 Introduction 4  \n1.0.1 Proposed Solution ........................ 4  \n1.0.2 Thesis Structure ......................... 5  \n2 Background 6  \n2.1 Software Architecture and its Knowledge ................ 6  \n2.2 Issues and Issue Tracking Software ................... 6  \n2.3 E􀀋ectiveness Metrics ........................... 7  \n2.3.1 Precision and Recall ....................... 7  \n2.3.2 NDCG ............................... 7  \n2.3.3 Issue Properties .......................... 8  \n2.3.4 Architectural Design Decisions (ADD) ............. 9  \n2.4 AK-Mining tools ............................. 9  \n3 Related Work 10  \n3.1 Representation of Architectural Design Decisions ........... 10  \n3.2 Retrospective Recovery of Architectural Design Decisions ....... 11  \n3.2.1 Using issue tracking systems ................... 11  \n4 Methodology 12  \n4.1 Issue collection strategy ......................... 12  \n4.1.1 Top-Down Issue Collection .................... 12  \n4.1.2 Bottom-Up Issue Collection ................... 13  \n4.2 Annotation ................................ 13  \n4.2.1 Iterative annotation process ................... 14  \n4.2.2 Number of issues ......................... 14  \n4.2.3 Di􀀋erences in the labeling process between the top-down and the bottom-up approaches .................... 14  \n4.3 Measuring the e􀀋ectiveness ........................ 15  \n4.3.1 Precision .............................. 15  \n4.3.2 NDCG ............................... 15  \n4.3.3 Missed Parents .......................... 15  \n4.4 ADD type distributions .......................... 15  \n4.5 Issue Property Distributions ....................... 16  \n5 Results 17  \n5.1 RQ1: Bottom up And Top Down E􀀋ectiveness ............. 17  \n5.1.1 Precision Results ......................... 17  \n5.1.2 NDCG Results .......................... 19  \n5.1.3 Missed Parents Results ...................... 21  \n5.2 RQ2: ADD Type distributions ...................... 22  \n5.3 Issue Property Analysis Results ..................... 25  \n5.3.1 RQ2: AK-Issues Only ...................... 25  \n5.3.2 RQ3: AK-Issues vs. Non-AK-Issues Results .......... 28  \n6 Discussion 30  \n6.1 How e􀀋ective are top-down and bottom-up approaches in 􀀌nding architectural issues in issue tracking systems? .............. 30  \n6.2 What are the di􀀋erences in AK characteristics and issue properties between the top-down and bottom-up approaches? ........... 30  \n6.3 What are the di􀀋erences in issue properties between AK issues and non-AK issues? .............................. 31  \n6.4 Threats to Validity ............................ 31  \n7 Conclusion 32  \n7.0.1 Future Work ............................ 32  \nA Code Book 36  \nA.1 General .................................. 36  \nA.1.1 Recurring architectural changes ................. 36  \nA.2 Existence Decisions (Ontocrisis) ..................... 36  \nA.2.1 Design Speci􀀌cations ....................... 36  \nA.2.2 New Architectural Component(s) ................ 37  \nA.2.3 Con􀀌guration descriptions .................... 37  \nA.2.4 Bans or Non-existence ...................... 37  \nA.3 Property Decisions (Diacrises) ...................... 37  \nA.4 Executive Decisions (Pericrises) ..................... 38  \nB Search Q","cbCaieJhm67cej1H","https://ap.wps.com/l/cbCaieJhm67cej1H","pdf",935331,"English","# 1 Introduction\n## 1.0.1 Proposed Solution\n## 1.0.2 Thesis Structure\n# 2 Background\n## 2.1 Software Architecture and its Knowledge\n## 2.2 Issues and Issue Tracking Software\n## 2.3 E\u0012ectiveness Metrics\n## 2.4 AK-Mining tools\n# 3 Related Work\n## 3.1 Representation of Architectural Design Decisions\n## 3.2 Retrospective Recovery of Architectural Design Decisions\n# 4 Methodology\n## 4.1 Issue collection strategy\n## 4.2 Annotation\n## 4.3 Measuring the e\u0012ectiveness\n## 4.4 ADD type distributions\n## 4.5 Issue Property Distributions\n# 5 Results\n## 5.1 RQ1: Bottom up And Top Down E\u0012ectiveness\n## 5.2 RQ2: ADD Type distributions\n## 5.3 Issue Property Analysis Results\n# 6 Discussion\n## 6.1 How e\u0012ective are top-down and bottom-up approaches\n## 6.2 What are the di\u0012erences in AK characteristics and issue properties\n## 6.4 Threats to Validity\n# 7 Conclusion\n## 7.0.1 Future Work\n# A Code Book\n## A.1 General\n## A.2 Existence Decisions (Ontocrisis)\n## A.3 Property Decisions (Diacrises)\n## A.4 Executive Decisions (Pericrises)\n# B Search Queries\n## B.1 Decision Factors\n## B.2 Reusable Solutions\n## B.3 Components and Connectors\n## B.4 Rationale","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"Recording and accessing architectural knowledge in a systematic way is difficult, and valuable design decisions can be lost over time. The thesis focuses on extracting such knowledge from issue tracking systems.\"},{\"question\":\"How is architectural knowledge extracted and prepared for analysis?\",\"answer\":\"Two tools for mining architectural knowledge from issues are evaluated, and extracted issues are annotated with architectural design decisions using a coding book developed for the annotation process.\"},{\"question\":\"How are the tools compared in terms of effectiveness?\",\"answer\":\"Effectiveness is measured using information-retrieval metrics such as precision and NDCG, along with additional analyses like missed parents and issue property distributions.\"}]","Mining Architectural Knowledge in Issue Tracking Systems - Thesis Abstract | PDF",1789233456]