Multi-algorithm for predicting the level accuracy of fault output in software

Article Sidebar

PDF
Published: Dec 2, 2024
DOI: https://doi.org/10.69598/sehs.18.24020006
Keywords:
software defect fault output expected multi-algorithm

Main Article Content

Zulkifli Zulkifli
Computer Science Department, Binus Graduate Program, Binus University, Jakarta, Indonesia. Corresponding author's e-mail: zulkifli003@binus.ac.id
Ford Lumban Gaol
Computer Science Department, Binus Graduate Program, Binus University, Jakarta, Indonesia
Agung Trisetyarso
Computer Science Department, Binus Graduate Program, Binus University, Jakarta, Indonesia
Widodo Budiharto
Computer Science Department, School of Computer Science, Binus University, Jakarta, Indonesia

Abstract

Software fault output refers to software errors found by testers during the software testing process. Software testing is an overly critical stage in software development; hence, a software testing model is required to systematically classify software errors. For the process of classifying software fault output, accuracy measurements are needed to predict manual fault outputs compared to algorithm-generated fault outputs. Algorithmic methods can be used to measure the accuracy of fault output. The main objective of this research was to compare different algorithms for predicting the accuracy of fault output on a dataset derived from past software testing. The findings indicate that the neural network algorithm outperforms SVM, MLP, RF, and MNB algorithms, achieving 98% accuracy when using 10 software testing variables (function, interface, structure, performance, requirement, documentation, positive, negative, basis path, and times) to predict expected fault outputs.

Downloads

Download data is not yet available.

Article Details

How to Cite
Zulkifli, Z., Lumban Gaol, F., Trisetyarso, A., & Budiharto, W. (2024). Multi-algorithm for predicting the level accuracy of fault output in software. Science, Engineering and Health Studies, 18, 24020006. https://doi.org/10.69598/sehs.18.24020006
Issue
Volume 18, 2024
Section
Physical sciences
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A. E., and Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.

Afzal, W., and Piadehbasmenj, A. (2021). Cloud-based architectures for model-based simulation testing of embedded software. In Proceedings of the 10th Mediterranean Conference on Embedded Computing (MECO), pp. 1–8. Budva, Montenegro.

Ali, S. A., Roy, N. R., and Raj, D. (2023). Software defect prediction using machine learning. In Proceedings of the 10th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 639–642. New Delhi, India.

Arcuri, A. (2020). Automated black-and white-box testing of RESTful APIs with EvoMaster. IEEE Software, 38(3), 72–78.

Aydin, I., Karaköse, M., and Akin, E. (2007). Artificial immune based support vector machine algorithm for fault diagnosis of induction motors. In Proceedings of the International Aegean Conference on Electrical Machines and Power Electronics, pp. 217–221. Bodrum, Turkey.

Beghoura, M. A., Boubetra, A., and Boukerram, A. (2017). Green software requirements and measurement: Random decision forests-based software energy consumption profiling. Requirements Engineering. 22(1), 27–40.

Catal, C., Alan, O., and Balkan, K. (2011). Class noise detection based on software metrics and ROC curves. Information Sciences, 181(21), 4867–4877.

Dhanda, N., Datta, S. S., and Dhanda, M. (2022). Machine learning algorithms. In Research Anthology on Machine Learning Techniques, Methods, and Applications (Khosrow-Pour, M., Ed.), pp. 849–869. Pennsylvania: IGI Global.

Dias-Neto, A. C., and Travassos, G. H. (2009). Model-based testing approaches selection for software projects. Information and Software Technology, 51(11), 1487–1504.

Du, K.-L., and Swamy, M. N. S. (2006). Neural Networks in a Softcomputing Framework, London: Springer. pp. 1–566.

El-Far, I. K., and Whittaker, J. A. (2018). Model-based software testing. In Encyclopedia of Software Engineering (Marciniak, J. J., Ed.), pp. 1–22. New Jersey: John Wiley & Sons.

Essebaa, I., Chantit, S., and Ramdani, M. (2019). Model-based testing from model driven architecture: A novel approach for automatic test cases generation. In Advances in Smart Technologies Applications and Case Studies. SmartICT 2019: Lecture Notes in Electrical Engineering, vol 684 (El Moussati, A., Kpalma, K., Ghaouth Belkasmi, M., Saber, M., Guégan, S., Eds.), pp. 600–609. Cham: Springer.

Hammouri, A., Hammad, M., Alnabhan, M. M., Alnabhan, M., and Alsarayrah, F. (2018). Software bug prediction using machine learning approach. International Journal of Advanced Computer Science and Applications, 9(2), 78–83.

Jean, A. K., Diarra, M., Bakary, B. A., Pierre, G., and Jérôme, A. K. (2022). Application based on hybrid CNN-SVM and PCA- SVM approaches for classification of cocoa beans. International Journal of Advanced Computer Science and Applications, 13(9), 231–238.

Latif, A., Fitriana, L. A., and Firdaus, M. R. (2021). Comparative analysis of software effort estimation using data mining technique and feature selection. Journal of Computer Science and Technology, 6(2), 167–174.

Lemos, O. A. L., Silveira, F. F., Ferrari, F. C., and Garcia, A. (2018). The impact of software testing education on code reliability: An empirical assessment. Journal of Systems and Software, 137, 497–511.

Lin, M., Chen, Q., and Yan, S. (2014). Network in network. In Proceedings of the 2nd International Conference on Learning Representations (ICLR) 2014, pp. 1–10. Banff, Canada.

Liu, S. (2020). An integrated scheme for online dynamic security assessment based on partial mutual information and iterated random forest. IEEE Transactions on Smart Grid, 11(4), 3606–3619.

Luo, R., Huang S., Chen, H., and Chen M. (2021). Code confusion in white box crowdsourced software testing. International Journal of Performability Engineering, 17(3), 276–288.

Masad, I. S., Alqudah, A., Alqudah, A. M., and Almashaqbeh, S. (2021). A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest x-ray images. International Journal of Electrical and Computer Engineering, 11(6), 5530–5540.

Massoudi, M., Jain, N. K., and Bansal, P. (2021). Software defect prediction using dimensionality reduction and deep learning. In Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, pp. 884–893. Tirunelveli, India.

Miholca, D. L. (2021). New conceptual cohesion metrics: Assessment for software defect prediction. In Proceedings of the 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), pp. 163–170. Timisoara, Romania.

Naresh, E., Naik, M. D., Niranjanamurthy, M., and Shankar, S. P. (2020). Functional testing on heart disease detection using multilayer perceptron technique. Journal of Computational and Theoretical Nanoscience, 17(9–10), 3915–3920.

Niu, W., Zhang, X., Du, X., Zhao, L., Cao, R., and Guizani, M. (2020). A deep learning based static taint analysis approach for IoT software vulnerability location. Measurement, 152, 107139.

Petry, K. L. (2020). Model-based testing of software product lines: Mapping study and research roadmap. Journal of Systems and Software, 167, 110608.

Ramírez-Méndez, J., Quesada-López, C., Martínez, A., and Jenkins, M. (2021). Agent-oriented approaches for model-based software testing: A mapping study. In Advances in Intelligent Systems and Computing (Rocha, Á., Ferrás, C., López-López, P. C., and Guarda, T., Eds.), pp. 340–349. Cham: Springer.

Ruuska, S., Hämäläinen, W., Kajava, S., Mughal, M., Matilainen, P., and Mononen, J. (2018). Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle. Behavioural Processes, 148, 56–62.

Sonalitha, E., Nurdewanto, B., Zubair, A., Asriningtias, S. R., Yudhistiro, K., and Mujahidin, I. (2020). Blackbox testing model boundary value of mapping taxonomy applications and data analysis of art and artworks. In Proceedings of the 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 7–11. Yogyakarta, Indonesia.

Thota, M. K., Shajin, F. H., and Rajesh, P. (2020). Survey on software defect prediction techniques. International Journal of Applied Science and Engineering, 17(4), 331–344.

Vanmali, M., Last, M., and Kandel, A. (2002). Using a neural network in the software testing process. International Journal of Intelligent Systems, 17(1), 45–62.

Zulkifli, Gaol, F. L., Trisetyarso, A., and Budiharto, W. (2022). Software testing model by measuring the level of accuracy fault output using neural network algorithm. In Proceedings of the 2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA), pp. 1–6. Yogyakarta, Indonesia.

Zulkifli, Z., Gaol, F. L., Trisetyarso, A., and Budiharto, W. (2023). Software testing integration-based model (I-BM) framework for recognizing measure fault output accuracy using machine learning approach. International Journal of Software Engineering and Knowledge Engineering, 33(08), 1149–1168.