Extracting structural information from bug reports
N Bettenburg, R Premraj, T Zimmermann… - Proceedings of the 2008 …, 2008 - dl.acm.org
Proceedings of the 2008 international working conference on Mining software …, 2008•dl.acm.org
In software engineering experiments, the description of bug reports is typically treated as
natural language text, although it often contains stack traces, source code, and patches.
Neglecting such structural elements is a loss of valuable information; structure usually leads
to a better performance of machine learning approaches. In this paper, we present a tool
called infoZilla that detects structural elements from bug reports with near perfect accuracy
and allows us to extract them. We anticipate that infoZilla can be used to leverage data from …
natural language text, although it often contains stack traces, source code, and patches.
Neglecting such structural elements is a loss of valuable information; structure usually leads
to a better performance of machine learning approaches. In this paper, we present a tool
called infoZilla that detects structural elements from bug reports with near perfect accuracy
and allows us to extract them. We anticipate that infoZilla can be used to leverage data from …
In software engineering experiments, the description of bug reports is typically treated as natural language text, although it often contains stack traces, source code, and patches. Neglecting such structural elements is a loss of valuable information; structure usually leads to a better performance of machine learning approaches. In this paper, we present a tool called infoZilla that detects structural elements from bug reports with near perfect accuracy and allows us to extract them. We anticipate that infoZilla can be used to leverage data from bug reports at a different granularity level that can facilitate interesting research in the future.
ACM Digital LibraryShowing the best result for this search. See all results