The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130680
PDF

Predicting Blocking Bugs with Machine Learning Techniques: A Systematic Review

Author 1: Selasie Aformaley Brown
Author 2: Benjamin Asubam Weyori
Author 3: Adebayo Felix Adekoya
Author 4: Patrick Kwaku Kudjo
Author 5: Solomon Mensah

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 6, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The application of machine learning (ML) techniques to predict blocking bugs have emerged for the early detection of Blocking Bugs (BBs) in software components to mitigate the adverse effect of BBs on software release and project cost. This study presents a systematic literature review of the trends in the application of ML techniques in BB prediction, existing research gaps, and possible research directions to serve as a reference for future research and an application insight for software engineers. We constructed search phrases from relevant terms and used them to extract peer-reviewed studies from the databases of five famous academic publishers, namely Scopus, SpringerLink, IEEE Xplore, ACM digital library, and ScienceDirect. We included primary studies published between January 2012 and February 2022 that applied ML techniques to building Blocking Bug Prediction models (BBPMs). Our result reveals a paucity of literature on BBPMs. Also, previous researchers employed ML techniques such as Decision Trees, Random Forest, Bayes Network, XGBoost, and DNN in building existing BB prediction models. However, the publicly available datasets for building BBPMs are significantly imbalanced. Despite the poor performance of the Accuracy metric where imbalanced datasets are concerned, some primary studies still utilized the Accuracy metric to assess the performance of their proposed BBPM. Further research is required to validate existing and new BBPM on datasets of commercial software projects. Also, future researchers should mitigate the effect of class imbalance on the proposed BB prediction model before training a BBPM.

Keywords: Blocking bugs; systematic review; software maintenance; bug report; reliability; machine learning

Selasie Aformaley Brown, Benjamin Asubam Weyori, Adebayo Felix Adekoya, Patrick Kwaku Kudjo and Solomon Mensah, “Predicting Blocking Bugs with Machine Learning Techniques: A Systematic Review” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130680

@article{Brown2022,
title = {Predicting Blocking Bugs with Machine Learning Techniques: A Systematic Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130680},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130680},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Selasie Aformaley Brown and Benjamin Asubam Weyori and Adebayo Felix Adekoya and Patrick Kwaku Kudjo and Solomon Mensah}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

16-17 April 2026

  • Berlin, Germany

Healthcare Conference 2026

21-22 May 2025

  • Amsterdam, The Netherlands

Computing Conference 2025

19-20 June 2025

  • London, United Kingdom

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2025

6-7 November 2025

  • Munich, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org