# Automated software bug severity classification using ensemble machine learning scheme: A real case study

## TL;DR

This paper introduces an automated system for classifying software bug severity using machine learning, tested on Persian language data from a real-world case study.

## Contribution

The novel contribution is an ensemble machine learning approach for Persian bug report classification, using real unstructured data from a case study.

## Key findings

- The proposed approach achieved high accuracy in classifying Persian software bug reports.
- The system significantly reduced classification time compared to manual methods.
- The method is adaptable to the unique characteristics of the Persian language.

## Abstract

Software bug report classification is one of the most significant processes in software development for determining the nature and severity of faults based on their causes and effects. In many projects, software experts implement this process manually, which requires exorbitant time and effort. Although there are a few studies on automatic bug report classification using machine learning techniques, they mainly focus on structured open-source datasets. This paper presents an ensemble learning approach utilizing various multiclass machine learning, text classification, and natural language processing techniques for automated software bug severity classification, with an application in the Persian language. This language, due to its unique characteristics, requires the adoption of different approaches from those applicable to the English language for text classification. The proposed approach utilizes a real bug dataset extracted from a case study containing unstructured bug reports. This dataset contains 4429 bug reports about the software product of the studied company, which is used by thousands of users in government and private organizations. These bug reports were recorded in Persian text by the testing team or software users, and then classified based on their severity through meetings of development team managers in the company. Results demonstrate that the developed appraoch is highly accurate and significantly faster than manual classification, which can dramatically decrease software development time and cost.