Contextual Learning of Software Error Patterns in Modern Development Environments

MOSES NDUNG'U & ANTHONY NDUNGU

Authors

MOSES NDUNG'U & ANTHONY NDUNGU KENYA Author

Keywords:

Contextual Learning, Integrated Development Environments (IDEs), Debugging, Software Quality, Developer Productivity

Abstract

Software development has evolved significantly with the advent of modern integrated development environments (IDEs) and advanced debugging tools. However, the complexity of software systems has concurrently increased, leading to more intricate and obscure error patterns. This paper explores the application of contextual learning techniques to identify and understand software error patterns within contemporary development environments. By leveraging machine learning and natural language processing (NLP) methods, we aim to enhance the accuracy and efficiency of error detection and resolution. Our study includes an analysis of error logs, source code, and environmental factors to develop a comprehensive model for predicting and categorizing software errors. Experimental results demonstrate the potential of our approach to improve debugging processes and reduce the time required for error resolution, thereby enhancing overall software quality and developer productivity.

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