Abstract: A computing device trains a recurrent neural network (RNN), using a balanced dataset, to predict whether logs input to the RNN are indicative of respective successful computer code or respective failed computer code, the balanced dataset comprising positive log examples and negative log examples from a continuous integration (CI) pipeline, the positive log examples labelled as being indicative of successful computer code, and the negative log examples labelled as being indicative of failed computer code. The computing device inputs a log to the RNN, and monitors evolution of belief predictions of the RNN, as the RNN is analyzing the log, according to successive regions of the log. The computing devices determines, based on the evolution of the belief predictions, that a given region of the log meets a log fatal error criterion condition, and outputs an indication of the given region.