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ISSN: 2643-6744

Current Trends in Computer Sciences & Applications

Review Article(ISSN: 2643-6744)

Duplicate Detection Models for Bug Reports of Software Triage Systems: A Survey

Volume 1 - Issue 5

Behzad Soleimani Neysiani* and Seyed Morteza Babamir

  • Author Information Open or Close
    • Department of Software Engineering, University of Kashan, Iran

    *Corresponding author:Department of Software Engineering, Faculty of Computer & Electrical Engineering, University of Kashan, Kashan, Esfahan, Iran

Received: November 22, 2019;   Published: December 17, 2019

DOI: 10.32474/CTCSA.2019.01.000123

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Abstract

Duplicate bug report detection (DBRD) is one of the significant problems of software triage systems, which receive end-user bug reports. DBRD needs automation using artificial intelligence techniques like information retrieval, natural language processing, text and data mining, and machine learning. There are two models of duplicate detection as follows: The first model uses machine learning techniques to learn the features of duplication between pairs of bug reports.
The second model called IR-based that use a similarity metric like REP or BM25F to rank top-k bug reports that are similar to a target bug report. The IR-based approach has identical behavior like the k-nearest neighborhood algorithm of machine learning. This study reviews a decade of duplicate detection techniques and their pros and cons. Besides, the metrics of their validationperformance will be studied.

Keywords: Duplicate Detection Model; Machine Learning; Bug Report

Abstract| Introduction| Methodologies of Automatic Duplicate Bug Report Detection| Conclusion| References|