Predicting application resiliency issues using machine learning techniques

A computer-implemented method for predicting resiliency of software applications, the method executed by one or more processors programmed to perform the method, the method comprising:

• for each of a plurality of first software applications:
• obtaining, by one or more processors, one or more software construction variables associated with the first software application;
• obtaining, by the one or more processors, one or more software operation variables associated with the first software application;
• and
• obtaining, by the one or more processors, an error rate for the first software application indicating a total number of errors divided by a number of transactions handled by the first software application;
• training, by the one or more processors, a machine learning model to predict a resiliency of a particular software application using (i) the one or more software construction variables associated with each of the plurality of first software applications, (ii) the one or more software operation variables associated with each of the plurality of first software applications, and (iii) the error rate for each of the plurality of first software applications, wherein the training includes performing a plurality of iterations of training until a trained machine learning model is generated having prediction accuracy above a threshold;
• for a second software application, obtaining, by the one or more processors, at least one software construction variable and at least one software operation variable associated with the second software application;
• applying, by the one or more processors, the trained machine learning model to the at least one software construction variable and the at least one software operation variable associated with the second software application to predict an error rate for the second software application and determine a resiliency for the second software application based upon the predicted error rate including determining a severity of resiliency issues for the second software application and determining a likelihood of resiliency issues for the second software application, wherein the severity of resiliency issues for the second software application is determined by comparing the predicted error rate to one or more error rate thresholds, and the likelihood of resiliency issues for the second software application is based on a likelihood of the prediction of the error rate for the second software application;
• and
• presenting, by the one or more processors, a display including (i) an indication of a risk score based on the severity and/or likelihood of resiliency issues for the second software application, (ii) indications of a plurality of risk factors which contributed to the risk score, and (iii) weighted contributions of each risk factor to the risk score.