IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Fault Prediction Modelling in Open Source Software Under Imperfect Debugging and Change-Point

Fault Prediction Modelling in Open Source Software Under Imperfect Debugging and Change-Point
View Sample PDF
Author(s): Shozab Khurshid (University of Kashmir, Srinagar, India), A. K. Shrivastava (Fortune Institute of International Business, New Delhi, India)and Javaid Iqbal (University of Kashmir, Srinagar, India)
Copyright: 2021
Pages: 17
Source title: Research Anthology on Recent Trends, Tools, and Implications of Computer Programming
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-3016-0.ch013

Purchase

View Fault Prediction Modelling in Open Source Software Under Imperfect Debugging and Change-Point on the publisher's website for pricing and purchasing information.

Abstract

Instant demand of products and services by technologically active users has increased the demand for open source software (OSS)-based applications. Unfortunately, with the complexity and lack of understanding of OSS-based systems, it becomes difficult for a testing team to remove the faults and the fault removal rate becomes low in comparison to what it should be. This also results in generating new faults during removal. Also, the rate at which the testing team detects/corrects fault need not be same during the entire process of testing due to various reasons viz. change in testing strategy, understanding of code, change in resources, etc. In the existing literature on OSS, authors have developed many models considering the above aspects separately. In this article, all of the above aspects have been combined to develop a general framework for predicting the number of faults in OSS. The comparison of eight models on the basis of their prediction capability on two well-known Open Source Software datasets is created and then ranked using normalized criteria distance approach.

Related Content

Preethi, Sapna R., Mohammed Mujeer Ulla. © 2023. 16 pages.
Srividya P.. © 2023. 12 pages.
Preeti Sahu. © 2023. 15 pages.
Vandana Niranjan. © 2023. 23 pages.
S. Darwin, E. Fantin Irudaya Raj, M. Appadurai, M. Chithambara Thanu. © 2023. 33 pages.
Shankara Murthy H. M., Niranjana Rai, Ramakrishna N. Hegde. © 2023. 23 pages.
Jothimani K., Bhagya Jyothi K. L.. © 2023. 19 pages.
Body Bottom