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Active hotspot: An issue-oriented model to monitor software evolution and degradation

  • Qiong Feng
  • , Yuanfang Cai
  • , Rick Kazman
  • , Di Cui
  • , Ting Liu
  • , Hongzhou Fang
  • Drexel University
  • University of Hawai'i at Mānoa
  • Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

Architecture degradation has a strong negative impact on software quality and can result in significant losses. Severe software degradation does not happen overnight. Software evolves continuously, through numerous issues, fixing bugs and adding new features, and architecture flaws emerge quietly and largely unnoticed until they grow in scope and significance when the system becomes difficult to maintain. Developers are largely unaware of these flaws or the accumulating debt as they are focused on their immediate tasks of address individual issues. As a consequence, the cumulative impacts of their activities, as they affect the architecture, go unnoticed. To detect these problems early and prevent them from accumulating into severe ones we propose to monitor software evolution by tracking the interactions among files revised to address issues. In particular, we propose and show how we can automatically detect active hotspots, to reveal architecture problems. We have studied hundreds of hotspots along the evolution timelines of 21 open source projects and showed that there exist just a few dominating active hotspots per project at any given time. Moreover, these dominating active hotspots persist over long time periods, and thus deserve special attention. Compared with state-of-the-art design and code smell detection tools we report that, using active hotspots, it is possible to detect signs of software degradation both earlier and more precisely.

Original languageEnglish
Title of host publicationProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages986-997
Number of pages12
ISBN (Electronic)9781728125084
DOIs
StatePublished - Nov 2019
Event34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, United States
Duration: 10 Nov 201915 Nov 2019

Publication series

NameProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

Conference

Conference34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
Country/TerritoryUnited States
CitySan Diego
Period10/11/1915/11/19

Keywords

  • Architecture debt
  • Software evolution

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