Multi-objective driven refactoring of a monolith application using reinforcement learning

A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:

• obtain multiple code modules of a monolith application and a plurality of conflicting metrics for determining a set of microservices for the monolith application;
• perform a reinforcement learning-based clustering process that iteratively generates a plurality of clusters comprising the code modules based at least in part on feedback provided for the plurality of conflicting metrics at each iteration, wherein the feedback comprises a reward vector having a plurality of dimensions, the plurality of dimensions corresponding to at least a portion of the plurality of conflicting metrics, wherein the reinforcement learning-based clustering process comprises training a neural network to perform a Monte Carlo tree search over a Markov decision tree, wherein the Markov decision tree comprises a root node representing a current state of the plurality of clusters and at least one leaf node representing a state where each of at least a portion of the code modules is assigned to a given one of the plurality of clusters;
• generate candidate microservices for the monolith application, wherein each candidate microservice corresponds to a different one of the plurality of clusters;
• output the generated candidate microservices to at least one of a system and a user;
• and
• automatically generate one or more deployable microservices for at least a portion of the candidate microservices, wherein at least one of the candidate microservices corresponds to a monolith web application programming interface, and wherein automatically generating the one or more deployable microservices comprises converting the monolith web application programming interface to a representational state transfer application programming interface.

A system comprising:

• a memory configured to store program instructions;
• a processor operatively coupled to the memory to execute the program instructions to:
• obtain multiple code modules of a monolith application and a plurality of conflicting metrics for determining a set of microservices for the monolith application;
• perform a reinforcement learning-based clustering process that iteratively generates a plurality of clusters comprising the code modules based at least in part on feedback provided for the plurality of conflicting metrics at each iteration, wherein the feedback comprises a reward vector having a plurality of dimensions, the plurality of dimensions corresponding to at least a portion of the plurality of conflicting metrics, wherein the reinforcement learning-based clustering process comprises training a neural network to perform a Monte Carlo tree search over a Markov decision tree, wherein the Markov decision tree comprises a root node representing a current state of the plurality of clusters and at least one leaf node representing a state where each of at least a portion of the code modules is assigned to a given one of the plurality of clusters;
• generate candidate microservices for the monolith application, wherein each candidate microservice corresponds to a different one of the plurality of clusters;
• output the generated candidate microservices to at least one of a system and a user;
• and
• automatically generate one or more deployable microservices for at least a portion of the candidate microservices, wherein at least one of the candidate microservices corresponds to a monolith web application programming interface, and wherein automatically generating the one or more deployable microservices comprises converting the monolith web application programming interface to a representational state transfer application programming interface.