Detecting Duplicate Bug Report Using Character N-Gram-Based Features

We present an approach to identify duplicate bug reports expressed in free-form text. Duplicate reports needs to be identified to avoid a situation where duplicate reports get assigned to multiple developers. Also, duplicate reports can contain complementary information which can be useful for bug fixing. Automatic identification of duplicate reports (from thousands of existing reports in a bug repository) can increase the productivity of a Triager by reducing the amount of time a Triager spends in searching for duplicate bug reports of any incoming report. The proposed method uses character N-gram-based model for the task of duplicate bug report detection. Previous approaches are word-based whereas this study investigates the usefulness of low-level features based on characters which have certain inherent advantages (such as natural-language independence, robustness towards noisy data and effective handling of domain specific term variations) over word-based features for the problem of duplicate bug report detection. The proposed solution is evaluated on a publicly-available dataset consisting of more than 200 thousand bug reports from the open-source Eclipse project. The dataset consists of ground-truth (pre-annotated dataset having bug reports tagged as duplicate by the Triager). Empirical results and evaluation metrics quantifying retrieval performance indicate that the approach is effective.

[1]  Björn Regnell,et al.  An experiment on linguistic tool support for consolidation of requirements from multiple sources in market-driven product development , 2006, Empirical Software Engineering.

[2]  Per Runeson,et al.  Detection of Duplicate Defect Reports Using Natural Language Processing , 2007, 29th International Conference on Software Engineering (ICSE'07).

[3]  Tao Xie,et al.  An approach to detecting duplicate bug reports using natural language and execution information , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[4]  Lyndon Hiew,et al.  Assisted Detection of Duplicate Bug Reports , 2006 .

[5]  James Mayfield,et al.  Character N-Gram Tokenization for European Language Text Retrieval , 2004, Information Retrieval.

[6]  Dale Schuurmans,et al.  Language and Task Independent Text Categorization with Simple Language Models , 2003, NAACL.

[7]  Jonathan I. Maletic,et al.  Journal of Software Maintenance and Evolution: Research and Practice Survey a Survey and Taxonomy of Approaches for Mining Software Repositories in the Context of Software Evolution , 2022 .

[8]  Siau-Cheng Khoo,et al.  A discriminative model approach for accurate duplicate bug report retrieval , 2010, 2010 ACM/IEEE 32nd International Conference on Software Engineering.

[9]  Gail C. Murphy,et al.  Who should fix this bug? , 2006, ICSE.

[10]  Nicholas Jalbert,et al.  Automated duplicate detection for bug tracking systems , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[11]  Efstathios Stamatatos,et al.  Words versus Character n-Grams for Anti-Spam Filtering , 2007, Int. J. Artif. Intell. Tools.

[12]  Trevor C. Kemp.  Automated detection of duplicate free-form English bug reports , 2009 .

[13]  Stan Matwin,et al.  Feature Engineering for Text Classification , 1999, ICML.

[14]  Jian Pei,et al.  Mining Software Engineering Data , 2007, ICSE Companion.

[15]  Vlado Keselj,et al.  Document clustering using character N-grams: a comparative evaluation with term-based and word-based clustering , 2005, CIKM '05.