BugSum: Deep Context Understanding for Bug Report Summarization
During collaborative software development, bug reports are dynamically maintained and evolved as a part of a software project. For a historical bug report with complicated discussions, an accurate and concise summary can enable stakeholders to reduce the time effort perusing the entire content. Existing studies on bug report summarization, based on whether supervised or unsupervised techniques, are limited due to their lack of consideration of the redundant information and disapproved standpoints among developers’ comments. Accordingly, in this paper, we propose a novel unsupervised approach based on deep learning network, called BugSum. our approach integrates an auto-encoder network for feature extraction with a novel metric (believability) to measure the degree to which a sentence is approved or disapproved within discussions. In addition, a dynamic selection strategy is employed to optimize the comprehensiveness of the auto-generated summary represented by limited words. Extensive experiments show that our approach outperforms 8 comparative approaches over two public datasets. In particular, the probability of adding controversial sentences that are clearly disapproved by other developers during the discussion, into the summary is reduced by up to 69.6%.
Tue 14 Jul Times are displayed in time zone: (UTC) Coordinated Universal Time change
01:30 - 02:30: Session 4: SummalizationResearch / ERA at ICPC Chair(s): Venera ArnaoudovaWashington State University | |||
01:30 - 01:45 Paper | Improved Code Summarization via a Graph Neural Network Research Alexander LeClairUniversity Of Notre Dame, Sakib HaqueUniversity of Notre Dame, Lingfei WuIBM Research, Collin McMillanUniversity of Notre Dame Pre-print Media Attached | ||
01:45 - 02:00 Paper | BugSum: Deep Context Understanding for Bug Report Summarization Research Haoran LiuNational University of Defense Technology, Yue YuCollege of Computer, National University of Defense Technology, Changsha 410073, China, Shanshan LiNational University of Defense Technology, Yong GuoNational University of Defense Technology, Deze WangNational University of Defense Technology, Xiaoguang MaoNational University of Defense Technology Media Attached | ||
02:00 - 02:15 Paper | A Human Study of Comprehension and Code Summarization Research Sean StapletonUniversity of Michigan, Yashmeet GambhirUniversity of Michigan, Alexander LeClairUniversity Of Notre Dame, Zachary Eberhart, Westley WeimerUniversity of Michigan, USA, Kevin LeachUniversity of Michigan, Yu HuangUniversity of Michigan Pre-print Media Attached | ||
02:15 - 02:30 Paper | Linguistic Documentation of Software History ERA Media Attached |