Bayesian Network analysis of software logs for data-driven software maintenance

Abstract

Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct data-driven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared.