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2018 Bug Localization by Using Information Retrieval and Machine Learning Algorithms

In large scale software applications, bug localization is a difficult and costly process. Many issues or bugs may be reported at both development and maintenance phase of software development lifecycle. Hence, it is important for developers to discover the location of the bug. In general, source codes and bug reports are used for identifying bug location with the help of Information Retrieval (IR) techniques. In this paper, we present an IR-based bug localization approach named BugSTAiR that uses structured information of source files, source code history, bug reports and bug similarity data if exists. To do best of our knowledge it is the first system developed for JavaScript source files. The experimental results show that accuracy of the system is promising (~%30 on Top 1) on file level bug localization.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Mustafa Ersahin Semih Utku Deniz Kilinc

328 990
Subject Area: Computer Science Broadcast Area: International Type: Oral Paper Language: English