Software Testing Effort Estimation Based on Machine Learning Techniques: Single and Ensemble Methods

Mohamed Hosni, Ibtissam Medarhri, Juan Manuel Carrillo de Gea

2024

Abstract

Delivering an accurate estimation of the effort required for software system development is crucial for the success of any software project. However, the software development lifecycle (SDLC) involves multiple activities, such as software design, software build, and software testing, among others. Software testing (ST) holds significant importance in the SDLC as it directly impacts software quality. Typically, the effort required for the testing phase is estimated as a percentage of the overall predicted SDLC effort, typically ranging between 10% and 60%. However, this approach poses risks as it hinders proper resource allocation by managers. Despite the importance of this issue, there is limited research available on estimating ST effort. This paper aims to address this concern by proposing four machine learning (ML) techniques and a heterogeneous ensemble to predict the effort required for ST activities. The ML techniques employed include K-nearest neighbor (KNN), Support Vector Regression, Multilayer Perceptron Neural Networks, and decision trees. The dataset used in this study was obtained from a well-known repository. Various unbiased performance indicators were utilized to evaluate the predictive capabilities of the proposed techniques. The overall results indicate that the KNN technique outperforms the other ML techniques, and the proposed ensemble showed superior performance accuracy compared to the remaining ML techniques.

Paper Citation

in Harvard Style

Hosni M., Medarhri I. and Manuel Carrillo de Gea J. (2024). Software Testing Effort Estimation Based on Machine Learning Techniques: Single and Ensemble Methods. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-716-0, SciTePress, pages 517-524. DOI: 10.5220/0013072400003838

in Bibtex Style

@conference{kdir24,
author={Mohamed Hosni and Ibtissam Medarhri and Juan Manuel Carrillo de Gea},
title={Software Testing Effort Estimation Based on Machine Learning Techniques: Single and Ensemble Methods},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2024},
pages={517-524},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013072400003838},
isbn={978-989-758-716-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Software Testing Effort Estimation Based on Machine Learning Techniques: Single and Ensemble Methods
SN - 978-989-758-716-0
AU - Hosni M.
AU - Medarhri I.
AU - Manuel Carrillo de Gea J.
PY - 2024
SP - 517
EP - 524
DO - 10.5220/0013072400003838
PB - SciTePress