A Study of Information Retrieval Approaches in Duplicate Bug Detection

Background/Objectives: The aim of this paper was to explore various information retrieval approaches applied for detecting duplicate bug reports. Methods/Statistical Analysis: We have determined Data pre-processing, Textual Analysis, Similarity measurement, classification and clustering methods applied on bug reports of various open source browsers for detecting duplicate bug reports. Findings: Information Retrieval Approaches provide an efficient way of detecting duplicate bug reports. The result of our study states that Recall and precision are the two important aspects of performance analysis of duplicate bug detection methods. We can achieve a precision of 99% and recall of 98%, by using both textual and categorical similarity measurements. Application/Improvements: As a consequence it decreases the time and effort spent on fixing the same bug repeatedly.