Multi-modal artificial intelligence root cause analysis

A method implemented in a data processing system for performing a root cause analysis, the method comprising:

• obtaining build logs that include information associated with a software build that experienced a build problem;
• generating a knowledge graph by analyzing the build logs using a knowledge graph generation unit, the knowledge graph identifying a relationship between various entities in the build logs;
• extracting a signature of a candidate root cause of the build problem from the knowledge graph, the signature representing a subset of nodes and edges of the knowledge graph;
• providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure, the graphical language model being trained to receive the signature of the candidate root cause and to predict a category of root cause failure from among a plurality of root cause failures based on the signature of the candidate root cause;
• constructing a prompt for a language model using a prompt construction unit, the prompt instructing the language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause predicted by the graphical language model;
• providing the prompt as an input to the language model;
• receiving the root cause failure analysis from the language model;
• and
• performing one or more actions in response to receiving the root cause failure analysis.

A data processing system comprising:

• a processor;
• and
• a machine-readable storage medium storing executable instructions that, when executed, cause the processor alone or in combination with other processors to perform operations of:
• obtaining logs that include information associated with a problem with software;
• generating a knowledge graph by analyzing the logs using a knowledge graph generation unit, the knowledge graph identifying a relationship between various entities in the logs;
• extracting a signature of a candidate root cause of the problem from the knowledge graph, the signature representing a subset of nodes and edges of the knowledge graph;
• providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure, the graphical language model being trained to receive the signature of the candidate root cause and to predict a category of root cause failure from among a plurality of root cause failures based on the signature of the candidate root cause;
• constructing a prompt for a language model using a prompt construction unit, the prompt instructing the language model to generate a root cause failure analysis that describes the root cause of the problem with the software, the prompt including the category of root cause predicted by the graphical language model;
• providing the prompt as an input to the language model;
• receiving the root cause failure analysis from the language model;
• and
• performing one or more actions in response to receiving the root cause failure analysis.