[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122703-en":3,"doc-seo-122703-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122703,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Analyzing Software Maintenance Through Machine Learning and Mining Software Repositories Approaches - Doctoral Dissertation","The rapid growth of software systems requires disciplined planning and continuous maintenance to keep code bases evolving across long time horizons. Without effective maintenance, systems become more complex, degrade in quality, and become difficult to sustain. Existing maintenance-focused techniques often rely on heuristic rules and can struggle with cross-project evaluation, while recent ML-based approaches have shown limited performance due to dataset size, feature limitations, within-project setups, or tool gaps for data collection. This dissertation applies Mining Software Repositories with machine learning to detect code smells, code quality, and issue labels, supported by optimized caching, efficient retrieval, and comparative modeling.","University of Texas at El Paso  \nScholarWorks@UTEP  \nOpen Access Theses & Dissertations  \n2023-05-01  \nAnalyzing Software Maintenance Through Machine Learning and Mining Software Repositories Approaches  \nSayed Mohsin Reza  \nUniversity of Texas at El Paso  \nFollow this and additional works at: [https://scholarworks.utep.edu/open_etd](https://scholarworks.utep.edu/open_etd)  \n Part of the Computer Sciences Commons  \nRecommended Citation  \nReza, Sayed Mohsin, \"Analyzing Software Maintenance Through Machine Learning and Mining Software Repositories Approaches\" (2023) . Open Access Theses & Dissertations. 3844.  \n[https://scholarworks.utep.edu/open_etd/3844](https://scholarworks.utep.edu/open_etd/3844)  \nThis is brought to you for free and open access by ScholarWorks@UTEP. It has been accepted for inclusion in Open Access Theses & Dissertations by an authorized administrator of ScholarWorks@UTEP. For more information, please contact [lweber@utep.edu](lweber@utep.edu).  \nANALYZING SOFTWARE MAINTENANCE THROUGH MACHINE LEARNING AND MINING SOFTWARE REPOSITORIES APPROACHES  \nSAYED MOHSIN REZA  \nDoctoral Program in Computer Science  \nAPPROVED:  \n\n| Mahmud Shahriar Hossain, Ph.D., Chair |\n| --- |\n| Yoonsik Cheon, Ph.D. |\n\nHugo Gutierrez, Ph.D.  \nStephen L. Crites, Jr. , Ph.D. Dean of the Graduate School  \nCopyright © by  \nSayed Mohsin Reza 2023  \nto my  \nMOTHER, FATHER and WIFE with love  \nANALYZING SOFTWARE MAINTENANCE THROUGH MACHINE LEARNING AND MINING SOFTWARE REPOSITORIES APPROACHES  \nby  \nSAYED MOHSIN REZA  \nDISSERTATION  \nPresented to the Faculty of the Graduate School of  \nThe University of Texas at El Paso  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDOCTOR OF PHILOSOPHY  \nDepartment of Computer Science  \nTHE UNIVERSITY OF TEXAS AT EL PASO  \nMay 2023  \nAcknowledgements  \nI express my deep gratitude to my advisor, Dr. Mahmud Shahriar Hossain, and my previous advisor, Dr. Omar Badreddin, for their unwavering support, motivation, and exceptional guidance throughout my Ph.D. journey. Their patience and wisdom have been invaluable in guiding me through the research and writing of this dissertation. I consider myself fortunate to have had such dedicated mentors who consistently pushed me to achieve my best.  \nI extend my sincere appreciation to my Ph.D. committee members, Dr. Yoonsik Cheon and Dr. Hugo Gutierrez, for their insightful comments and feedback. Their contributions have been instrumental in shaping this work and have helped me broaden my research perspectives. I am grateful for their dedication, time, and commitment to making this work a success.  \nI am also grateful to the professors and staff at the University of Texas at El Paso Computer Science Department for their hard work and dedication in providing me with the means to complete my degree and prepare for a career as a computer scientist. Additionally, I am grateful to my family, especially my parents, for their unwavering love, support, and encouragement throughout my academic journey. Their constant motivation has been a source of strength and inspiration for me.  \nFinally, I am incredibly grateful to my dear wife, Laila Noor, for her unwavering support and understanding during my Ph.D. Her continuing, loving support without complaint has been invaluable to me.  \nAbstract  \nThe rapid growth of software systems demands meticulous planning and maintenance to accommodate the evolution of the code base over extended periods. Without maintenance, software systems will become more complex, low in quality, and hence unsustainable. Software engineers who perform maintenance often strive to optimize code quality or minimize code smells in a timely manner. Several techniques have been used to detect code quality or code smells as a part of software maintenance. Most of these techniques are based on heuristics, which create detection rules using a few metrics. These approaches have reasonable accuracy but do not work in cross-project evaluation. The rec","cbCaidbigB4SciNs","https://ap.wps.com/l/cbCaidbigB4SciNs","pdf",5706051,1,166,"English","en",105,"# Acknowledgements\n# Abstract\n# Dissertation Overview","[{\"question\":\"Why does software maintenance become critical as systems evolve over time?\",\"answer\":\"Software systems must be carefully maintained to accommodate continuous code-base evolution. Without maintenance, systems grow more complex and decrease in quality, becoming unsustainable.\"},{\"question\":\"What limitations affect traditional and recent code-quality or code-smell detection approaches?\",\"answer\":\"Heuristic-based techniques use a limited set of metrics and may not generalize well in cross-project evaluation. Recent ML efforts have also produced unsatisfactory results due to small datasets, fewer input features, within-project classification, and limited data-collection tooling.\"},{\"question\":\"How does the dissertation address code smells, code quality, and issue labels?\",\"answer\":\"It uses Mining Software Repositories (MSR) combined with machine learning to identify code smells and quality attributes from metrics, and to classify issue labels using neural-network-based methods, supported by optimized caching and efficient open-source data retrieval.\"}]","Analyzing Software Maintenance Through Machine Learning and Mining Software Repositories Approaches - Doctoral Dissertation | PDF",1785812379,418,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"analyzing-software-maintenance-through-machine-learning-and-mining-software-repositories-approaches-doctoral-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/analyzing-software-maintenance-through-machine-learning-and-mining-software-repositories-approaches-doctoral-dissertation/122703/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does software maintenance become critical as systems evolve over time?","Question",{"text":75,"@type":76},"Software systems must be carefully maintained to accommodate continuous code-base evolution. Without maintenance, systems grow more complex and decrease in quality, becoming unsustainable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations affect traditional and recent code-quality or code-smell detection approaches?",{"text":80,"@type":76},"Heuristic-based techniques use a limited set of metrics and may not generalize well in cross-project evaluation. 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