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Fast Duplicate Bug Reports Detector Training using Sampling for Dimension Reduction

Authors :

Behzad Soleimani Neysiani¹ Saeed Doostali² Seyed Morteza Babamir³ Zahra Aminoroaya⁴

1- دانشگاه کاشان 2- دانشگاه کاشان 3- دانشگاه کاشان 4- موسسه آموزش عالی علامه نائیتی

Keywords :

Information Retrieval, Natural Language Processing, Duplicate Detection, Bug Reports, Instance-based Learning, Online Query, Continuous Query, Incremental Learning

Abstract :

Duplicate bug report detection (DBRD) is an excellent problem in software triage systems like Bugzilla. It is vital to update the internal machine learning models of DBRD for real-world usage and continuous query of new bug reports. The training phase of machine learning algorithms is time-consumable and dependent on the volume of the training dataset. Instance-based learning (IbL) is a machine learning algorithm that reduces the number of samples in the training dataset to achieve fast learning for the incremental database. This research introduces a hybrid approach using clustering and straight forward sampling to improve the runtime and validation performance of DBRD. Two bug report datasets of Android and Mozilla Firefox are used to evaluate the proposed approach. The experimental evaluation shows acceptable results and improvement in both runtime and validation performance of DBRD versus traditional approach without IbL.

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