Impact analysis of bug localization accuracy oriented to bug report

Abstract

The study of bug report-oriented program error location has the characteristics of strong pertinence and low cost, which is an important direction in the current research on program error location. This type of research takes bug reports and source code as input sources, and establishes a mapping relationship between the two through semantic mapping strategies to locate program errors. In the fine-grained program error location scenario, there is a problem that the location accuracy is greatly reduced. Existing empirical studies analyze the difference in location accuracy from two aspects: input source data noise and semantic mapping strategy selection, but most studies take the established location tools and methods as the evaluation object, the evaluation data type is single, and there is a lack of fine-grained analysis of constructing key variables. In order to evaluate the influence of key variables of location method on location accuracy, this paper decouples the location method through pseudo-siamese network, measures the sensitivity of location accuracy by counting the gain of location accuracy under different input source data types, and adds input source data types and a variety of semantic mapping strategies, Based on the evaluation of 23808 bug reports and corresponding source code data in 7 open source projects published on JIRA, this paper provides a more detailed empirical basis for additional data type selection and weight allocation, combined learning of multiple data types and different semantic mapping strategies in fine-grained program error location.