Determination of support costs for a scheduled release of a product

A computer program product (CPP), the CPP comprising:

• a set of one or more computer-readable storage media;
• program instructions, collectively stored in the set of one or more computer-readable storage media, for causing a processor set to perform the following computer operations:
• use systems of an environment to obtain historical data of different predetermined types;
• query support repositories on which the obtained historical data of the different predetermined types is stored by the systems;
• build a first support profile for a first predetermined product that is scheduled to be released but not yet released, wherein the first support profile is based on the historical data, wherein the first predetermined product is computer software code that is centrally hosted on a computing platform and allocated on a subscription basis as Software as a Service (SaaS);
• calculate, based on the first support profile, a number of expected escapes for at least a first functional area of the computer software code,
• wherein the calculating the number of expected escapes for at least the first functional area of the computer software code includes testing at least the first functional area of the computer software code to determine an impact of the scheduled release;
• predict a total number of customer cases that will result from the scheduled release;
• add an indication of the predicted total number of customer cases in a cost report about the scheduled release of the computer software code;
• and
• output the cost report to a machine learning algorithm, wherein the machine learning algorithm uses the cost report to determine, based on predetermined release thresholds of processing resource capabilities within the computing platform, whether to release the computer software code,
• wherein the use of the cost report additionally includes the machine learning model determining how to allocate hardware and software licenses, wherein the allocation includes allocating the resources within the computing platform to a first functional area to mitigate a predicted risk that the first functional area is predicted to be subject to,
• wherein the historical data details cases about a second predetermined product that is different than the first predetermined product, wherein the historical data is logged for a plurality of different functional areas including the first functional area, wherein the functional areas include hardware components that are added to predetermined infrastructure, wherein the second predetermined product has at least a predetermined degree of similarity with the first predetermined product,
• wherein predicting a total number of customer cases includes using a predetermined conditional probability technique to predict total number of customer cases,
• wherein the predetermined conditional probability technique is based on Bayes Theorem, wherein a variable of a formula used in the predetermined conditional probability technique represents data from a weighted predetermined support evaluation process, wherein the predetermined support evaluation process is based on a predetermined development team's choice of an industry standard that is to be applied as a quality metric for the predetermined conditional probability technique, wherein a weighting of the predetermined support evaluation process is equal to a number of defects that exist per 1000 lines of code of the first predetermined product plus a test efficiency indicator (TEI) value, wherein the TEI value results from performing a predetermined TEI process for calculating the number of expected escapes for at least the first functional area of the computer software code.

A computer system (CS), the CS comprising:

• a processor set;
• a set of one or more computer-readable storage media;
• program instructions, collectively stored in the set of one or more storage media, for causing the processor set to perform the following computer operations:
• use systems of an environment to obtain historical data of different predetermined types;
• query support repositories on which the obtained historical data of the different predetermined types is stored by the systems;
• build a first support profile for a first predetermined product that is scheduled to be released but not yet released, wherein the first support profile is based on the historical data, wherein the first predetermined product is computer software code that is centrally hosted on a computing platform and allocated on a subscription basis as Software as a Service (SaaS);
• calculate, based on the first support profile, a number of expected escapes for at least a first functional area of the computer software code,
• wherein the calculating the number of expected escapes for at least the first functional area of the computer software code includes testing at least the first functional area of the computer software code to determine an impact of the scheduled release;
• predict a total number of customer cases that will result from the scheduled release;
• add an indication of the predicted total number of customer cases in a cost report about the scheduled release of the computer software code;
• and
• output the cost report to a machine learning algorithm, wherein the machine learning algorithm uses the cost report to determine, based on predetermined release thresholds of processing resource capabilities within the computing platform, whether to release the computer software code,
• wherein the use of the cost report additionally includes the machine learning model determining how to allocate hardware and software licenses, wherein the allocation includes allocating the resources within the computing platform to a first functional area to mitigate a predicted risk that the first functional area is predicted to be subject to,
• wherein the historical data details cases about a second predetermined product that is different than the first predetermined product, wherein the historical data is logged for a plurality of different functional areas including the first functional area, wherein the functional areas include hardware components that are added to predetermined infrastructure, wherein the second predetermined product has at least a predetermined degree of similarity with the first predetermined product,
• wherein predicting a total number of customer cases includes using a predetermined conditional probability technique to predict total number of customer cases,
• wherein the predetermined conditional probability technique is based on Bayes Theorem, wherein a variable of a formula used in the predetermined conditional probability technique represents data from a weighted predetermined support evaluation process, wherein the predetermined support evaluation process is based on a predetermined development team's choice of an industry standard that is to be applied as a quality metric for the predetermined conditional probability technique, wherein a weighting of the predetermined support evaluation process is equal to a number of defects that exist per 1000 lines of code of the first predetermined product plus a test efficiency indicator (TEI) value, wherein the TEI value results from performing a predetermined TEI process for calculating the number of expected escapes for at least the first functional area of the computer software code.