Software product development defect and issue prediction and diagnosis

A computer implemented method for software product development defect and issue prediction and diagnosis, the method comprising:

• ascertaining worker profile information that includes roles of a plurality of workers involved in development of a product;
• implementing at least one sensor to monitor physiology of the plurality of workers;
• ascertaining, from the at least one sensor, worker state information of the physiology of the plurality of workers with respect to the development of the product;
• generating, based on the worker profile information and the worker state information, a general worker signature that includes a plurality of clusters for all of the plurality of workers;
• for each of the plurality of workers, generating, based on worker profile information for an individual worker, worker state information for the individual worker, and the general worker signature, an individual worker signature vector that represents at least one cluster of the plurality of clusters that the individual worker is aligned to;
• ascertaining product state information on a status of temporal development for the product;
• generating, based on the product state information, a product signature vector that represents changes in the product;
• and
• generating, based on a neural network model based analysis of the individual worker signature vectors and the product signature vector over a temporal dimension, an output that includes an explanation for a defect associated with the development of the product.

A non-transitory computer readable medium having stored thereon machine readable instructions for software product development defect and issue prediction and diagnosis, the machine readable instructions, when executed, cause at least one hardware processor to:

• ascertain worker profile information that includes roles of a plurality of workers involved in development of a product;
• invoke at least one sensor to monitor physiology of the plurality of workers;
• ascertain, from the at least one sensor that monitors physiology of the plurality of workers, worker state information of the physiology of the plurality of workers with respect to the development of the product;
• generate, based on the worker profile information and the worker state information, a general worker signature that includes a plurality of clusters for all of the plurality of workers;
• for each of the plurality of workers, generate, based on worker profile information for an individual worker, worker state information for the individual worker, and the general worker signature, an individual worker signature vector that represents at least one cluster of the plurality of clusters that the individual worker is aligned to;
• ascertain product state information on a status of temporal development for the product;
• generate, based on the product state information, a product signature vector that represents changes in the product;
• and
• generate, based on a neural network model based analysis of the individual worker signature vectors and the product signature vector over a temporal dimension, an output that includes an explanation for a defect associated with the development of the product.