Software fault detection method based on micro-service log

Abstract

The invention provides a software fault detection method based on a micro-service log. The method comprises the following steps that the micro-service key degrees are ranked through a topology detection model; more important the micro-service is, the more possibly faults of the micro-service occur; the expenditure caused by blind one-by-one micro-service fault analysis and detection is reduced; then the low fault detection efficiency caused by machine learning each time is avoided through the mode matching service, and finally a machine learning model capable of classifying whether the micro-service is the fault service or not is constructed through extraction, dimension reduction and learning of log text features, so that the automation degree of operation and maintenance is improved.

Description

technical field

[0001] The invention relates to the technical field of software engineering, and more specifically, to a software fault detection method based on a microservice log. Background technique

[0002] In the fault detection methods of traditional distributed architecture systems, most of them adopt the point monitoring strategy. Point monitoring is to arrange monitoring points in key nodes and set thresholds, so that after a fault or abnormality occurs, the alarm information of the node can be used. Perform troubleshooting. Although microservices are also a type of distributed architecture, there are many nodes in the microservice architecture, complex links, frequent container startup and destruction, excessive monitoring costs for deployment points, and often associated error messages, making it impossible to accurately locate key faulty services . In addition to point monitoring, there is also a surface monitoring method that combines time correlation and lin...

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