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DTSTART:20241126T150000Z
UID:185025@events.unl.edu
DTSTAMP:20241114T220229Z
ORGANIZER;CN=unknown:
SUMMARY:Ph.D. Dissertation Defense\: Salomé Perez-Rosero
STATUS:CONFIRMED
DESCRIPTION:"Mining Work Items to Streamline Software Maintenance Tasks"\n\
 nSoftware engineering maintenance tasks often require associating code cha
 nges into groupings of related units of work to have as much information a
 s possible about the developments toward addressing a specific code task. 
 A comprehensive understanding of how a code task has evolved helps develop
 ers make better decisions about changes in the overall codebase\, where a 
 commit represents the set of code changes made to the codebase at a specif
 ic time. While the concept of work items as logically related code changes
  has been primarily theoretical\, its impact on software maintenance tasks
 \, such as tracing the origins of bugs or fixes spanning multiple commits 
 while scanning through real-world software repositories' commit histories\
 , remains unexplored. This thesis introduces heuristic-based algorithms to
  mine work items from commit histories in open-source repositories across 
 different scenarios. First\, when issue tags are available throughout the 
 commit history\, we developed our first heuristic that mines associations 
 out of validated issue tags from issue tracker systems such as Jira and Gi
 tHub. We generated a dataset of approximately 130\,000 work items across r
 epositories written in Java\, Kotlin\, and Python\, with each work item gr
 oup having a numerical confidence score for relatedness. Second\, in scena
 rios where a reference commit is known and the goal is to generate work it
 ems associated with it\, we developed our second heuristic that implements
  a method-level tracking mechanism. This approach scans the repository’s
  commit history backward\, identifying overlapping code modifications link
 ed to the reference commit to generate related work items. Third\, when an
  automated and fast way for identifying work items is needed\, we explore 
 using pre-trained LLMs with prompts containing different levels of detail 
 from commit diffs and logs to classify commit pairs as related or unrelate
 d work items. Alongside this\, we generate two work item datasets with lab
 eled ground truth for fine-tuning purposes. Finally\, we apply our top-per
 forming work item heuristic to a software maintenance task in the context 
 of the SZZ algorithms\, which aim to track a bug's introducing commit for 
 a given fix commit. Specifically\, we built a new SZZ variant that integra
 tes work item awareness\, which generated the first empirical evidence tha
 t bugs and fixes constitute work items\; and reported a 4-18% improvement 
 in bug-introducing commit identification over traditional SZZ algorithms. 
 \n\nCommittee\:\nDr. Robert Dyer and Dr. Witty Srisa-an\, Advisors\nDr. Bo
 nita Sharif\nDr. Lisong Xu\nDr. Qiuming Yao\nDr. Yi Qian
LOCATION:Avery Hall Room 103C, and online at Zoom (https://unl.zoom.us/j/96
 936787085)
URL://events.unl.edu/cse/2024/11/26/185025/
DTEND:20241126T170000Z
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