Analytically generated micro-service consumer-driven contracts and automated tests

1. A method, comprising:

obtaining a plurality of usage data records for a plurality of consumers of a micro-service from a gateway of a run-time environment, wherein the plurality of usage data records comprises real-time function call information for said micro-service;

extracting one or more data features from each of the usage data records, the one or more data features comprising: a plurality of service function call names associated with said real-time function call information and one or more service function parameters of each of said plurality of service function call names;

applying, using at least one processing device, a clustering algorithm to said usage data records to assign said usage data records to one of a plurality of usage pattern clusters based on said one or more extracted data features, wherein each of said plurality of usage pattern clusters comprises usage data records, and wherein said plurality of usage pattern clusters comprises:

at least one existing usage pattern cluster associated with one or more existing Consumer-Driven Contracts that define consumer expectations of the micro-service, with respect to an existing usage pattern associated with the existing usage pattern cluster, and

a new usage pattern cluster not associated with said one or more existing Consumer-Driven Contracts;

performing the following steps when the clustering algorithm creates said new usage pattern cluster:

creating a new Consumer-Driven Contract that defines consumer expectations of the micro-service, with respect to a new usage pattern associated with the new usage pattern cluster, wherein the new Consumer-Driven Contract is indicative of one or more data structure formats of responses provided from said micro-service in response to corresponding consumer requests; and

generating one or more automated Consumer-Driven Contract tests to verify whether responses sent from said micro-service conform to the data structure format in the new Consumer-Driven Contract.

2. The method of claim 1 , further comprising the step of storing one or more of said new Consumer-Driven Contract, said automated Consumer-Driven Contract tests and results of said automated Consumer-Driven Contract tests in a data store.

3. The method of claim 1 , wherein said obtaining step further comprises obtaining run-time operations data from the run-time environment, wherein said steps of creating and generating are based at least in part on said run-time operations data.

4. The method of claim 1 , wherein the following steps further comprise the step of generating anonymous data to validate the new usage pattern.

5. The method of claim 1 , wherein the new usage pattern is validated using anonymous data in a closed system loop without human access.

6. The method of claim 1 , wherein the applying step is performed at a plurality of states of a software development life cycle of the micro-service.

7. The method of claim 1 , wherein the applying step further comprises the step of applying one or more of a machine learning algorithm and an anomaly detection algorithm to the usage data records to identify one or more patterns in the usage data records.

8. The method of claim 1 , further comprising the step of applying a Supervised Machine Learning Classification/Clustering technique to labeled usage data records to train an analytical model.

9. A system, comprising:

a memory; and

at least one processing device, coupled to the memory, operative to implement the following steps:

obtaining a plurality of usage data records for a plurality of consumers of a micro-service from a gateway of a run-time environment, wherein the plurality of usage data records comprises real-time function call information for said micro-service;

extracting one or more data features from each of the usage data records, the one or more data features comprising: a plurality of service function call names associated with said real-time function call information and one or more service function parameters of each of said plurality of service function call names;

applying, using at least one processing device, a clustering algorithm to said usage data records to assign said usage data records to one of a plurality of usage pattern clusters based on said one or more extracted data features, wherein each of said plurality of usage pattern clusters comprises usage data records, and wherein said plurality of usage pattern clusters comprises:

at least one existing usage pattern cluster associated with one or more existing Consumer-Driven Contracts that define consumer expectations of the micro-service, with respect to an existing usage pattern associated with the existing usage pattern cluster, and

a new usage pattern cluster not associated with said one or more existing Consumer-Driven Contracts;

performing the following steps when the clustering algorithm creates said new usage pattern cluster:

creating a new Consumer-Driven Contract that defines consumer expectations of the micro-service, with respect to a new usage pattern associated with the new usage pattern cluster, wherein the new Consumer-Driven Contract is indicative of one or more data structure formats of responses provided from said micro-service in response to corresponding consumer requests; and

generating one or more automated Consumer-Driven Contract tests to verify whether responses sent from said micro-service conform to the data structure format in new Consumer-Driven Contract.

10. The system of claim 9 , further comprising the step of storing one or more of said new Consumer-Driven Contract, said automated Consumer-Driven Contract tests and results of said automated Consumer-Driven Contract tests in a data store.

11. The system of claim 9 , wherein said obtaining step further comprises obtaining run-time operations data from the run-time environment, wherein said steps of creating and generating is based at least in part on said run-time operations data.

12. The system of claim 9 , wherein the following steps further comprise the step of generating anonymous data to validate the new usage pattern.

13. The system of claim 9 , wherein the new usage pattern is validated using anonymous data in a closed system loop without human access.

14. The system of claim 9 , wherein the applying step is performed at a plurality of states of a software development life cycle of the micro-service.

15. The system of claim 9 , wherein the applying step further comprises the step of applying one or more of a machine learning algorithm and an anomaly detection algorithm to the usage data records to identify one or more patterns in the usage data records.

16. The system of claim 9 , further comprising the step of applying a Supervised Machine Learning Classification/Clustering technique to labeled usage data records to train an analytical model.

17. A computer program product, comprising a non-transitory machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps:

obtaining a plurality of usage data records for a plurality of consumers of a micro-service from a gateway of a run-time environment, wherein the plurality of usage data records comprises real-time function call information for said micro-service;

extracting one or more data features from each of the usage data records, the one or more data features comprising: a plurality of service function call names associated with said real-time function call information and one or more service function parameters of each of said plurality of service function call names;

applying, using at least one processing device, a clustering algorithm to said usage data records to assign said usage data records to one of a plurality of usage pattern clusters based on said one or more extracted data features, wherein each of said plurality of usage pattern clusters comprises usage data records, and wherein said plurality of usage pattern clusters comprises:

at least one existing usage pattern cluster associated with one or more existing Consumer-Driven Contracts that define consumer expectations of the micro-service, with respect to an existing usage pattern associated with the existing usage pattern cluster, and

a new usage pattern cluster not associated with said one or more existing Consumer-Driven Contracts;

performing the following steps when the clustering algorithm creates said new usage pattern cluster:

creating a new Consumer-Driven Contract that defines consumer expectations of the micro-service, with respect to a new usage pattern associated with the new usage pattern cluster, wherein the new Consumer-Driven Contract is indicative of one or more data structure formats of responses provided from said micro-service in response to corresponding consumer requests; and

generating one or more automated Consumer-Driven Contract tests to verify whether responses sent from said micro-service conform to the data structure format in new Consumer-Driven Contract.

18. The computer program product of claim 17 , wherein said obtaining step further comprises obtaining run-time operations data from the run-time environment, wherein said steps of creating and generating is based at least in part on said run-time operations data.

19. The computer program product of claim 17 , wherein the following steps further comprise the step of generating anonymous data to validate the new usage pattern.

20. The computer program product of claim 17 , wherein the new usage pattern is validated using anonymous data in a closed system loop without human access.