Machine Learning Based Predictive Analysis of Software Bug Severity and Priority


  • 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


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


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.                


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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



Research Article