Title: Multi-label classification and fuzzy similarity-based expert identification techniques for software bug assignment

Authors: Rama Ranjan Panda; Naresh Kumar Nagwani

Addresses: Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, National Institute of Technology-Raipur, Chhattisgarh, India

Abstract: In software development, a bug can occur due to multiple failures in software, and it may require multiple developers to fix it. In machine learning approaches, the bugs are assigned to a developer with a clear-cut outcome based on the agreed level of opinion from the assigner. However, instances of software bugs are textual and fuzzy. In this paper, two fuzzy systems: the fuzzy bug assignment technique for software developers and unique term relationships (FDUR) and the fuzzy bug assignment technique for software developers and category relationships (FDCR) are developed to measure the degree of relationships between developers, bugs, and its categories. The computed degree of relationship is used for handling the bugs with multiple categories and a set of developers involved in the development of software. To measure and compare the performance of both techniques with other existing techniques, the experiments are carried out on the benchmark software repositories.

Keywords: bug assignment; expert finding; decision making; fuzzy logic; mining bug repositories; machine learning; fuzzy similarity.

DOI: 10.1504/IJCSE.2024.142837

International Journal of Computational Science and Engineering, 2024 Vol.27 No.6, pp.734 - 748

Received: 03 Aug 2023
Accepted: 23 Jan 2024
Published online: 28 Nov 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article