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Machine Learning And Deep Learning Based Approaches For Detecting Duplicate Bug Reports With Stack Traces

Title:

Machine Learning And Deep Learning Based Approaches For Detecting Duplicate Bug Reports With Stack Traces

Ebrahimi Koopaei, Neda (2019) Machine Learning And Deep Learning Based Approaches For Detecting Duplicate Bug Reports With Stack Traces. PhD thesis, Concordia University.

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Abstract

Many large software systems rely on bug tracking systems to record the submitted bug reports and to track and manage bugs. Handling bug reports is known to be a challenging task, especially in software organizations with a large client base, which tend to receive a considerable large number of bug reports a day. Fortunately, not all reported bugs are new; many are similar or identical to previously reported bugs, also called duplicate bug reports.
Automatic detection of duplicate bug reports is an important research topic to help reduce the time and effort spent by triaging and development teams on sorting and fixing bugs. This explains the recent increase in attention to this topic as evidenced by the number of tools and algorithms that have been proposed in academia and industry. The objective is to automatically detect duplicate bug reports as soon as they arrive into the system. To do so, existing techniques rely heavily on the nature of bug report data they operate on. This includes both structural information such as OS, product version, time and date of the crash, and stack traces, as well as unstructured information such as bug report summaries and descriptions written in natural language by end users and developers.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science
Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (PhD)
Authors:Ebrahimi Koopaei, Neda
Institution:Concordia University
Degree Name:Ph. D.
Program:Electrical and Computer Engineering
Date:17 July 2019
Thesis Supervisor(s):Hamou-Lhadj, Abdelwahab
ID Code:985972
Deposited By: NEDA EBRAHIMI KOOPAEI
Deposited On:14 Nov 2019 20:27
Last Modified:14 Nov 2019 20:27
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