BEGIN:VCALENDAR METHOD:REQUEST PRODID:Microsoft Exchange Server 2010 VERSION:2.0 BEGIN:VTIMEZONE TZID:Eastern Standard Time BEGIN:STANDARD DTSTART:16010101T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=1SU;BYMONTH=11 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010101T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;INTERVAL=1;BYDAY=2SU;BYMONTH=3 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ORGANIZER;CN="Hunter, Tiffany":mailto:huntert1@ohio.edu ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN=Rrezarta.K rasniqi@unt.edu:mailto:Rrezarta.Krasniqi@unt.edu ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Abukamail, Nasseef":mailto:abukamai@ohio.edu ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Allwine, D aniel":mailto:allwined@ohio.edu ATTENDEE;ROLE=REQ-PARTICIPANT;PARTSTAT=NEEDS-ACTION;RSVP=TRUE;CN="Bartone, C hris":mailto:bartone@ohio.edu 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DESCRIPTION;LANGUAGE=en-US:[cid:image002.png@01DA7F95.57E6F760]\nRrezarta K rasniqi\, Ph.D.\nUniversity of North Texas\n\n\n[cid:image004.jpg@01DA7F95 .57E6F760]Biography:\nRrezarta Krasniqi recently earned a Ph.D. in the Dep artment of Computer Science\nand Engineering at the University of North Te xas. Her research interests lie in\nSoftware Engineering\, specifically Re quirements Engineering\, with an emphasis on\nSoftware Maintenance and Evo lution. In her research\, she primarily focuses on\nproblems related to th e detection of quality-related bugs. She develops tools and\ntechniques to enhance and contextualize the understanding of complex quality\nissues re lated to security\, usability\, reliability among others that emerge as by products\nof long-term maintenance efforts. Her research has resulted in 1 2 firstauthored\nconference proceedings and 2 first-authored journal artic les\, among\nothers. One of her recent papers has been selected as a top b est paper in SANER'21\nand was invited and accepted for publication in the prestigious EMSE'23 Journal. Last year\, she has been\nawarded a doctoral research grant to further support her doctoral studies and received the b est poster award at\nthe Tapia'23 conference. Her work has also been featu red in the public press for receiving a People's Choice\nAward in the 3MT Doctoral Competition and for being selected as a finalist for the NCWIT Co llegiate Award.\nCurrently\, she is serving as a Program Committee member for ICSE\, SANER\, CAIN\, and EASE\, including as\na reviewer for the Soft ware Quality Journal and the Journal for Software: Evolution and Process. She holds a\nB.S. degree in Computer Science and Mathematics from the Univ ersity of Prishtina\, an M.S. degree in Computer\nScience from Midwestern State University\, and an M.S. degree in Computer Science and Engineering from the\nUniversity of Notre Dame.\nAbstract:\nExploring the Software Qua lity Maze: Detecting Scattered and Tangled Quality Concerns in Source\nCod e to Aid Maintenance Tasks\nSoftware quality attributes\, such as reliabil ity\, security\, and usability\, are often well-defined and understood at\ nthe requirement level. They lay the ground foundation necessary to achiev e high-quality\, robust\, user-friendly\,\nand trustworthy software system s. However\, when addressing these attributes at the code level\, two sign ificant\nchallenges emerge. First\, they tend to scatter across the codeba se due to improper encapsulation of objectoriented\nclasses\, hampering th e visibility of quality-related components across the codebase. Second\, t hey\nbecome tangled within a single module due to intricate interdependenc ies with functional aspects of the code.\nAddressing quality concerns in t he presence of scattered and tangled code can lead to unforeseen issues. F or\nexample\, software developers may inadvertently introduce new and late nt bugs or incorrectly implement code\ncomponents deviating from the origi nal system-wide requirements. To tackle these pressing issues\, my researc h\nproposes a series of state-of-the-art solutions integrating ML-based te chniques and NLP-based techniques\,\nincluding static program analysis tec hniques to automatically and effectively detect\, repair\, and untangle qu ality\nconcerns present at the code level.\nIn this talk\, I will discuss two recent projects on this topic. First\, I will introduce a multi-layere d framework\nthat combines Machine Learning\, Information Retrieval\, and Natural Language Processing that automatically\ndetects several intertangl ed quality-related issues present in bug report code descriptions. Next\, I will present a\ncontext-aware bug fixing framework that can cluster scat tered quality concerns into a meaningful hierarchy\,\ninferring a set of c andidate classes relevant for recommending quality bug repairs. Finally\, I will discuss the\nfuture research plan\, where my focus will be on captu ring the evolving nature of quality concerns during\nsoftware maintenance and evolution\, aiming to advance intelligent tools that enhance human cap abilities. By\ncapturing contextual information from various artifacts\, t ask dependencies\, and historical data\, my long-term\ngoal is to improve the productivity of knowledge workers\, including software developers.\n\n \n________________________________________________________________________ ________\nMicrosoft Teams Need help?
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