Duplicate and similar bug report detection and retrieval using neural networks

A method, comprising:

• receiving, by a device, information associated with a first entity and a second entity to be classified as duplicate entities or non-duplicate entities;
• identifying, by the device, a first set of descriptions, associated with the first entity, and a second set of descriptions associated with the second entity,
• each description, included in the first set of descriptions, sharing a description type with a corresponding description included in the second set of descriptions;
• encoding, by the device, the first set of description into a first set of vectors using a corresponding set of neural networks,
• each neural network, of the corresponding set of neural networks, operating on a different description type;
• encoding, by the device, the second set of descriptions into a second set of vectors using the corresponding set of neural networks;
• classifying, by the device, the first entity and the second entity as duplicate entities or non-duplicate entities based on the first set of vectors and the second set of vectors;
• and
• performing, by the device, an action based on classifying the first entity and the second entity as duplicate entities or non-duplicate entities.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive information associated with a first entity and a second entity to be classified as duplicate entities or non-duplicate entities;
• identify a first description associated with the first entity and a second description associated with the second entity,
• the first description and the second description having a first description type;
• identify a third description associated with the first entity and a fourth description associated with the second entity,
• the third description and the fourth description having a second description type that is different from the first description type,
• the third description being different from the first description, and
• the fourth description being different from the second description;
• encode the first description into a first vector using a first neural network;
• encode the second description into a second vector using the first neural network;
• encode the third description into a third vector using a second neural network;
• encode the fourth description into a fourth vector using the second neural network;
• classify the first entity and the second entity as duplicate entities or non-duplicate entities based on the first vector, the second vector, the third vector, and the fourth vector;
• and
• perform an action based on classifying the first entity and the second entity as duplicate entities or non-duplicate entities.