Automated program and software defect root cause analysis using machine learning techniques

Abstract

For the automated root cause analysis (ARCA) method and simplified RCA technique, their empirical assessment is presented in this study. A focus group meeting is a foundation for the target problem identification in the ARCA technique. This is compared to earlier RCA methodologies which rely on problem sampling for target problem discovery and high beginning costs. In this research, we suggest a naïve Bayes based machine learning method for identifying the underlying causes of newly reported software issues, which will facilitate a quicker and more effective resolution of software bugs. The ARCA technique produced a large number of high-quality corrective actions while requiring a reasonable amount of effort. The strategy is an effective way to find new opportunities for process improvement and produce fresh process improvement ideas in contrast to the organization’s corporate practices. In addition it is simple to utilize. Ultimately, we compared the methodology with other machine learning classifiers including support vector machine and decision tree.

Keywords

• Software defect prediction (SDP)
• machine learning
• RCA
• problem prevention
• naïve Bayes