Advaita: Bug Duplicity Detection System
- URL: http://arxiv.org/abs/2001.10376v1
- Date: Fri, 24 Jan 2020 04:48:39 GMT
- Title: Advaita: Bug Duplicity Detection System
- Authors: Amit Kumar, Manohar Madanu, Hari Prakash, Lalitha Jonnavithula,
Srinivasa Rao Aravilli
- Abstract summary: Duplicate bugs rate (% of duplicate bugs) are in the range from single digit (1 to 9%) to double digits (40%) based on the product maturity, size of the code and number of engineers working on the project.
Detecting duplicity deals with identifying whether any two bugs convey the same meaning.
This approach considers multiple sets of features viz. basic text statistical features, semantic features and contextual features.
- Score: 1.9624064951902522
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Bugs are prevalent in software development. To improve software quality, bugs
are filed using a bug tracking system. Properties of a reported bug would
consist of a headline, description, project, product, component that is
affected by the bug and the severity of the bug. Duplicate bugs rate (% of
duplicate bugs) are in the range from single digit (1 to 9%) to double digits
(40%) based on the product maturity , size of the code and number of engineers
working on the project. Duplicate bugs range are between 9% to 39% in some of
the open source projects like Eclipse, Firefox etc. Detection of duplicity
deals with identifying whether any two bugs convey the same meaning. This
detection of duplicates helps in de-duplication. Detecting duplicate bugs help
reduce triaging efforts and saves time for developers in fixing the issues.
Traditional natural language processing techniques are less accurate in
identifying similarity between sentences. Using the bug data present in a bug
tracking system, various approaches were explored including several machine
learning algorithms, to obtain a viable approach that can identify duplicate
bugs, given a pair of sentences(i.e. the respective bug descriptions). This
approach considers multiple sets of features viz. basic text statistical
features, semantic features and contextual features. These features are
extracted from the headline, description and component and are subsequently
used to train a classification algorithm.
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