Abstract
Bug report summarization provides an outline of the present status of the bug to developers. The reason behind highlighting the solution of individual reported bug is to bring up the most appropriate solution and important data to resolve the bug. This technique basically limits the amount of time that the developer spent in a bug report maintenance activity. The previous researches show that till date the bug report summaries are not up to the developer expectations and they still have to study the whole bug report. So, in order to overcome this downside, bug report summarization method is proposed in light of collection of comments instead of single comment. The informative and phraseness feature are extracted from the bug reports to generate the all possible subsets of summary. These summary subsets are evaluated by Particle Swarm Optimization (PSO) to achieve the best subset. This approach is compared with the existing Bug Report Classifier (BRC) and Email Classifier (EC). For all approaches, the ROUGE score was calculated and compared with three human-generated summaries of 10 bug reports of Rastkar dataset. It was observed that the summary subset evaluated by PSO was more effective and generated less redundant, noise reduction summary and covered all the important points of bug reports due to its semantic base analysis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
