Machine Learning Based Predictive Analysis of Software Bug Severity and Priority

Authors

  • Kamna Vaid Research Scholar, University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India
  • Udayan Ghose University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

Keywords:

Predictive Analysis, Predictive Models, Software Bugs, Priority, Severity, SVM, Random Forest Classifier

Abstract

Software fault prediction is a vital and helpful technique for boosting the quality and dependability of software. There exists the prospective to enhance project management by proactively estimating prospective release delays and implementing cost-effective measures to boost software quality. This can be achieved by forecasting the components within a sizable software system that are most likely to exhibit a significant number of flaws in subsequent releases. However, creating reliable fault prediction models is a difficult task. This study’s primary goal is to carry out an investigation into the predictive analysis of software development frameworks with regard to software bug attributes: severity and priority. The machine learning method utilized in this study was implemented by using the Python programming platform. The implementation of this study makes use of methods from AI, along with data mining, and Machine Learning, along with statistical algorithms, and also modelling. Prediction models can be of assistance in maximizing all of the resources needed for the research. Random Forest (RF) Classifier and Support Vector Machine (SVM) are two techniques used in machine learning model training to determine the severity and urgency of the problem.  Per the findings of the study, The RF Priority Model provides a detailed outlook of the model's predicted performance across different priority levels with an accuracy rate of 0.87. This investigation assists developers discover faults based on existing software metrics using data mining techniques, which eventually will lead to an improvement in software quality and a decrease in the cost of developing software during both the development phase and the maintenance phase.                

Downloads

Download data is not yet available.

References

Ali, S., Baseer, S., Abbasi, I. A., Alouffi, B., Alosaimi, W., & Huang, J. (2022). Analyzing the interactions among factors affecting cloud adoption for software testing: a two-stage ISM-ANN approach. Soft Computing, 26(16), 8047-8075

Shatnawi, M. Q., & Alazzam, B. (2022). An Assessment of Eclipse Bugs' Priority and Severity Prediction Using Machine Learning. International Journal of Communication Networks and Information Security, 14(1), 62-69.

Akmel, F., Birihanu, E., & Siraj, B. (2017). A literature review study of software defect prediction using machine learning techniques. Int. J. Emerg. Res. Manag. Technol, 6(6), 300-306.

Ali, S., Ullah, N., Abrar, M. F., Majeed, M. F., Umar, M. A., & Huang, J. (2019). Barriers to software outsourcing partnership formation: an exploratory analysis. IEEE Access, 7, 164556-164594.

Ali, S., Nasir, A., Samad, A., Basser, S., & Irshad, A. (2022). An automated approach for the prediction of the severity level of bug reports using GPT-2. Security and Communication Networks, 2022.

Olaleye, T. O., Arogundade, O. T., Abayomi-Alli, A., & Adesemowo, A. K. (2021). An ensemble predictive analytics of COVID-19 infodemic tweets using bag of words. In Data science for COVID-19 (pp. 365-380). Academic Press.

Zhang, Y., Chen, Y., Cheung, S. C., Xiong, Y., & Zhang, L. (2018, July). An empirical study on TensorFlow program bugs. In Proceedings of the 27th ACM SIGSOFT international symposium on software testing and analysis (pp. 129-140).

Malgonde, O., & Chari, K. (2019). An ensemble-based model for predicting agile software development effort. Empirical Software Engineering, 24, 1017-1055.

Shetty, J., Babu, B. S., & Shobha, G. (2020). Proactive cloud service assurance framework for fault remediation in cloud environment. International Journal of Electrical & Computer Engineering (2088-8708), 10(1).

Ahmed, H. A., Bawany, N. Z., & Shamsi, J. A. (2021). Capbug-a framework for automatic bug categorization and prioritization using nlp and machine learning algorithms. IEEE Access, 9, 50496-50512.

Pachouly, J., Ahirrao, S., Kotecha, K., Selvachandran, G., & Abraham, A. (2022). A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools. Engineering Applications of Artificial Intelligence, 111, 104773.

Alsaedi, S. A., Noaman, A. Y., Gad-Elrab, A. A., & Eassa, F. E. (2023). Nature-Based Prediction Model of Bug Reports Based on Ensemble Machine Learning Model. IEEE Access.

Alsghaier, H., & Akour, M. (2021). Software fault prediction using whale algorithm with genetics algorithm. Software: Practice and Experience, 51(5), 1121-1146.

J.Kim and G. Yang, ‘‘Bug severity prediction algorithm using topic based feature selection and CNN-LSTM algorithm,’’ IEEE Access, vol. 10, pp. 94643–94651, 2022.

https://www.usenix.org/system/files/login/articles/login_aug15_08_gunawi.pdfY.

Tian, D. Lo, X. Xia and C. Sun, "Automated prediction of bug report priority using multi-factor analysis", Empirical Software Engineering, vol. 20, no. 5, pp. 1354-1383, 2015.

Downloads

Published

07.02.2024

How to Cite

Vaid, K. ., & Ghose, U. . (2024). Machine Learning Based Predictive Analysis of Software Bug Severity and Priority . International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 249–256. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4740

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.