Stack Trace

Stack traces, sequences of function calls leading to software errors, are increasingly studied to understand and address bugs, particularly in the rapidly expanding field of machine learning. Current research focuses on analyzing patterns within stack traces to identify common error sources and improve debugging processes, often employing deep learning models like recurrent neural networks and convolutional neural networks for tasks such as predicting the most appropriate developer to fix a bug (bug triage). This work is significant because it moves beyond simple error classification, leveraging the rich information in stack traces to improve software reliability and developer efficiency.