Scholarlink

HotBugs.jar is a novel benchmark targeting time-critical (a.k.a. hot) fixes. We propose an approach to analyze the taxonomy of the bugs in HotBugs.jar by extending PatchCat into HotCat, integrating hotfix metadata with multi-objective optimization. Using NSGA-II, we evolve bitmask-based feature subsets that balance accuracy, Normalized Mutual Information (NMI), and runtime. On 88 records across 17 categories, HotCat achieved 0.59 accuracy and 0.58 NMI in 129 s, with maximum accuracy of 0.63 in 132 s, demonstrating accuracy improvements without additional resource use, thus supporting sustainability. Future work will expand and augment the dataset, refine optimization objectives, and improve semantic categorization, robustness, and cluster balance.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

HotCat: Green and Effective Feature Selection toward Hotfix Bug Taxonomy

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Luis de la Cal,
  • Yazhuo Cao,
  • Ayse Irmak Ercevik,
  • Giovanni Pinna,
  • Lukas Twist,
  • David Williams,
  • Karine Even-Mendoza,
  • W. B. Langdon,
  • Hector D. Menendez,
  • Federica Sarro

摘要

HotBugs.jar is a novel benchmark targeting time-critical (a.k.a. hot) fixes. We propose an approach to analyze the taxonomy of the bugs in HotBugs.jar by extending PatchCat into HotCat, integrating hotfix metadata with multi-objective optimization. Using NSGA-II, we evolve bitmask-based feature subsets that balance accuracy, Normalized Mutual Information (NMI), and runtime. On 88 records across 17 categories, HotCat achieved 0.59 accuracy and 0.58 NMI in 129 s, with maximum accuracy of 0.63 in 132 s, demonstrating accuracy improvements without additional resource use, thus supporting sustainability. Future work will expand and augment the dataset, refine optimization objectives, and improve semantic categorization, robustness, and cluster balance.