Implementing Centralized Error Handling for Software Systems through the Integration of Machine Learning Techniques
Abstract
Thomson Alexander
Based on the increase of data volumes in the current world, modern software solutions' complexities and transaction volume make it imperative to establish more efficient and robust error-handling approaches. Traditional strategies have struggled to cope with the dynamics of voluminous transactions known to be decentralized and ad-hoc. This has led to operational disruptions and diverse software efficacy disruptions. The central prism of this paper points to how to leverage a proposed central error-handling system (CEHS). The proposition encapsulates how machine learning techniques can be leveraged to address these challenges. Discussing the limitations of current error-handling methods and highlighting benefits stemming from a centrist approach are embedded in this discourse. Consequently, this thread explores integrating ML algorithms as a prerequisite for identifying anomalies, triggering remedial actions, and predicting errors that could stem from the scenario. The remit of this discourse is to present a framework for CEHS implementation and discuss the potential impact regarding the reliability and performance of high transactional volumes through the prism of novel approaches in ML like data science skills and software engineering skills.