Title: Fuzzy modelling techniques for improving multi-label classification of software bugs

Authors: Rama Ranjan Panda; Naresh Kumar Nagwani

Addresses: Department of Computer Science and Engineering, National Institute of Technology-Raipur, Chhattisgarh, India ' Department of Computer Science and Engineering, National Institute of Technology-Raipur, Chhattisgarh, India

Abstract: Software bug repositories stores a wealth of information related to the problems that occurred during the software development. Today's software development is a modular approach, with multiple developers working in different locations all around the world. A software bug may belong to multiple categories and can be resolved by more than one developer. For understanding the multiple causes of software bugs and proper bug information management at large bug repositories, better classification of software bugs is needed. In the proposed work, a multi-label fuzzy system-based classification (ML-FBC) is proposed. A fuzzy system is used to compute the membership of software bugs into multiple categories. Then a fuzzy c-means clustering algorithm is used to create various clusters. Once the clusters are created, the cluster-category mapping is done for various software bugs. For a new bug, the fuzzy similarity values are computed, and the created cluster-category mappings are utilised to categorise it. Using a user-defined threshold value, a new bug is classified into multi-label categories. Experiments are carried out on available benchmark datasets to compare the performance measures F1 score, BEP score, Hloss, accuracy, training time, and testing time of various multi-label classifiers. The proposed ML-FBC outperforms existing multi-label classifiers.

Keywords: mining bug repositories; bug information management; fuzzy modelling; multi-class categorisation; multi-label classification.

DOI: 10.1504/IJICA.2023.131355

International Journal of Innovative Computing and Applications, 2023 Vol.14 No.3, pp.141 - 154

Received: 24 Sep 2021
Received in revised form: 03 Feb 2022
Accepted: 30 May 2022
Published online: 07 Jun 2023 *