Estimating propagation time for an injected fault

A method, comprising:

• determining, by a processor set, normal execution times of respective services in a call graph of an application;
• determining, by the processor set, normal execution times of respective network communications between ones of the services;
• determining, by the processor set, faulty execution times of respective ones of the services;
• and
• generating, by the processor set, a propagation time for a particular type of fault injected at a particular fault injection location in the call graph based on the determined normal execution times of respective services, the determined normal execution times of respective network communications, and the determined faulty execution times of respective ones of the services, wherein the generating the propagation time is further based on one or more parallelization numbers.

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

• determine normal execution times of respective services in a call graph of an application, wherein nodes of the call graph represent microservices in the application and paths between the nodes represent direction of request flow between the microservices;
• determine normal execution times of respective network communications between ones of the services;
• determine faulty execution times of respective ones of the services by running fault injection simulations;
• and
• generate a propagation time for a particular type of fault injected at a particular fault injection location in the call graph based on the determined normal execution times of respective services, the determined normal execution times of respective network communications, and the determined faulty execution times of respective ones of the services,
• wherein the program instructions are further executable to:
• repeat the generating for each of plural different types of faults and each of plural different fault injection locations;
• and
• generate a list comprising multiple entries each comprising:
• a respective one of the plural different types of faults;
• a respective one of the plural different fault injection locations;
• and
• the generated propagation time for a combination of the respective one of the plural different types of faults and the respective one of the plural different fault injection locations;
• and
• wherein the program instructions are further executable to:
• generate training data based on the list;
• and
• train a machine learning model using the training data, wherein the machine learning model is configured to predict a fault type and a fault location of the application based on an input comprising log data of the application.