Systems and methods to identify breaking application program interface changes

• US 10,379,995 B1
• Filed: 03/22/2019
• Issued: 08/13/2019
• Est. Priority Date: 07/06/2018
• Status: Active Grant

• Current Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Original Assignee: Capital One Services LLC (Capital One Financial Corporation)
• Inventors: Walters, Austin, Goodsitt, Jeremy, Pham, Vincent, Key, Kate
• Primary Examiner(s): Jeon, Jae U
• Application Number:
  US16/362,466
• Time in Patent Office: 144 Days
• CPC Class Codes: G06F 11/3608 using formal methods, e.g. model checking, abstract interpretation theorem proving G06N5/013
• CPC Class Codes: G06F 11/3628 of optimised code optimisation G06F8/443
• CPC Class Codes: G06F 11/3636 by tracing the execution of the program
• CPC Class Codes: G06F 11/3684 for test design, e.g. generating new test cases
• CPC Class Codes: G06F 11/3688 for test execution, e.g. scheduling of test suites
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 16/2237 Vectors, bitmaps or matrices
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• CPC Class Codes: G06F 16/2423 Interactive query statement specification based on a database schema
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 16/258 Data format conversion from or to a database
• CPC Class Codes: G06F 16/283 Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/90332 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 16/9038 : Presentation of query results
• CPC Class Codes: G06F 16/906 : Clustering; Classification
• CPC Class Codes: G06F 16/93 : Document management systems
• CPC Class Codes: G06F 17/15 : Correlation function computation including computation of convolution operations arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00
• CPC Class Codes: G06F 17/16 : Matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization matrix transposition G06F7/78
• CPC Class Codes: G06F 17/18 : for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 18/2115 : by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
• CPC Class Codes: G06F 18/213 : Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06F 18/214 : Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/2148 : characterised by the process organisation or structure, e.g. boosting cascade
• CPC Class Codes: G06F 18/217 : Validation; Performance evaluation; Active pattern learning techniques
• CPC Class Codes: G06F 18/2193 : based on specific statistical tests
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23 : Clustering techniques
• CPC Class Codes: G06F 18/24 : Classification techniques
• CPC Class Codes: G06F 18/2411 : based on the proximity to a decision surface, e.g. support vector machines
• CPC Class Codes: G06F 18/2415 : based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
• CPC Class Codes: G06F 18/285 : Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
• CPC Class Codes: G06F 18/40 : Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06F 21/552 : involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/60 : Protecting data
• CPC Class Codes: G06F 21/6245 : Protecting personal data, e.g. for financial or medical purposes
• CPC Class Codes: G06F 21/6254 : by anonymising data, e.g. decorrelating personal data from the owner's identification
• CPC Class Codes: G06F 30/20 : Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06F 40/117 : Tagging; Marking up details of markup languages G06F40/143; Designating a block; Setting of attributes style sheets, e.g. eXtensible Stylesheet Language Transformation [XSLT], G06F40/154
• CPC Class Codes: G06F 40/166 : Editing, e.g. inserting or deleting
• CPC Class Codes: G06F 40/20 : Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06F 8/71 : Version control security arrangements therefor G06F21/57; Configuration management
• CPC Class Codes: G06F 9/54 : Interprogram communication
• CPC Class Codes: G06F 9/541 : via adapters, e.g. between incompatible applications
• CPC Class Codes: G06F 9/547 : Remote procedure calls [RPC]; Web services
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 20/10 : using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 : Ensemble learning
• CPC Class Codes: G06N 3/04 : Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/047 : Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/06 : Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 3/084 : Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/088 : Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/094 : Adversarial learning
• CPC Class Codes: G06N 5/00 : Computing arrangements using knowledge-based models
• CPC Class Codes: G06N 5/01 : Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/02 : Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 : Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 7/00 : Computing arrangements based on specific mathematical models
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/04 : Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06T 11/001 : Texturing; Colouring; Generation of texture or colour inpainting G06T5/77
• CPC Class Codes: G06T 2207/10016 : Video; Image sequence
• CPC Class Codes: G06T 2207/10024 : Color image
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/20084 : Artificial neural networks [ANN]
• CPC Class Codes: G06T 7/194 : involving foreground-background segmentation
• CPC Class Codes: G06T 7/246 : using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/248 : involving reference images or patches
• CPC Class Codes: G06T 7/254 : involving subtraction of images
• CPC Class Codes: G06V 10/768 : using context analysis, e.g. recognition aided by known co-occurring patterns
• CPC Class Codes: G06V 10/993 : Evaluation of the quality of the acquired pattern
• CPC Class Codes: G06V 30/194 : References adjustable by an adaptive method, e.g. learning
• CPC Class Codes: G06V 30/1985 : Syntactic analysis, e.g. using a grammatical approach syntactic image representation G06V30/18171
• CPC Class Codes: H04L 63/1416 : Event detection, e.g. attack signature detection
• CPC Class Codes: H04L 63/1491 : using deception as countermeasure, e.g. honeypots, honeynets, decoys or entrapment
• CPC Class Codes: H04L 67/306 : User profiles
• CPC Class Codes: H04L 67/34 : involving the movement of software or configuration parameters network booting or remote initial program loading [RIPL] G06F9/4416
• CPC Class Codes: H04N 21/23412 : for generating or manipulating the scene composition of objects, e.g. MPEG-4 objects
• CPC Class Codes: H04N 21/8153 : comprising still images, e.g. texture, background image

Abstract

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include sending a first call to a first node-testing model associated with a first API and receiving a first model output comprising a first model result and a first model-result category. The operations may include identifying a second node-testing model associated with a second API and sending a second call to the second node testing model. The operations may include receiving a second model output comprising a second model result and a second model-result category. The operations may include performing at least one of sending a notification, generating an updated first node-testing model, generating an updated second node-testing model, generating an updated first call, or generating an updated second call.

Claims

1. A system for managing application programming interfaces (APIs), comprising:

one or more memory units storing instructions; and

one or more processors that execute the instructions to perform operations comprising;

sending a first call to a first node-testing model associated with a first API;

receiving from the first node-testing model, a first model output based on the first call, the first model output comprising a first model result and a first model-result category;

receiving API call data associated with a plurality of APIs;

training a routing model to identify routing pathways between API nodes using the API call data;

sending the first model result to the routing model;

after sending the first model result, receiving, from the routing model, information identifying a second node-testing model associated with a second API;

sending a second call to the second node-testing model, the second call being based on the first model result;

receiving, from the second node-testing model, a second model output based on the second call, the second model output comprising a second model result and a second model-result category;

and

performing at least one of;

generating an updated first node-testing model based on the first node-testing model when the first model result category comprises a failure or an unknown result;

generating an updated second node-testing model based on the second node-testing model when the second model result category comprises a failure or an unknown result;

generating, by a first translation model, an updated first call when the first model result category comprises a failure;

or

generating, by a second translation model, an updated second call when the second model result category comprises a failure.

2. The system of claim 1, the operations further comprising:

receiving API call data based on the first API; and

training the first node-testing model using the API call data.

3. The system of claim 1, the operations further comprising:

displaying, at an interface, at least one of the first model result, the first model-result category, the second model result, or the second model-result category.

4. The system of claim 3 wherein the operations further comprise:

receiving user inputs from the interface; and

performing at least one of;

generating an updated first node-testing model;

generating an updated second node-testing model;

generating an updated first call;

or

generating an updated second call based on the user inputs.

5. The system of claim 1, wherein at least one of the first translation model or second translation model comprises a machine-learning model.

6. The system of claim 1, wherein at least one of the first node-testing model or second node-testing model comprises a machine-learning model.

7. The system of claim 1, the operations further comprising:

sending the first call to the updated first node-testing model;

receiving from the updated first node-testing model, an updated first model output based on the first call, the updated first model output comprising;

an updated first model result; and

an updated first model-result category; and

performing, based on at least one of the updated first model result or updated first model-result category, at least one of;

sending a notification to a client device based on at least one of the updated first model result or the updated first model-result category;

training the updated first node-testing model using API call data;

or

generating an updated first call.

8. The system of claim 1, the operations further comprising:

sending the second call to the updated second node-testing model;

receiving from the updated second node-testing model, an updated second model output based on the second call, the updated second model output comprising an updated second model result and an updated second category of the updated second model result; and

performing, based on at least one of the updated second model result or updated second category, at least one of;

sending a notification to a client device based on at least one of the updated second model result or the updated second category;

training the updated second node-testing model using API call data;

or

generating an updated second call.

9. The system of claim 1, the operations further comprising receiving, from the client device, a request comprising the first call.

10. The system of claim 1, wherein:

the operations further comprise receiving from a third node-testing model, a third model output comprising a third model result; and

the first call is based on the third call result.

11. The system of claim 1, wherein a cloud service performs the at least one of sending the notification to the device, generating the updated first node-testing model, generating the updated second node-testing model, generating the updated first call, or generating the updated second call.

12. The system of claim 1, wherein a local computer performs the at least one of sending the notification to the device, generating the updated first node-testing model, generating the updated second node-testing model, generating the updated first call, or generating the updated second call.

13. The system of claim 1, wherein at least one of the first model result or second model result comprises an error message.

14. The system of claim 1, wherein at least one of:

the first call result comprises a statement indicating the first model result is an unknown result;

or

the second call result comprises a statement indicating the second model result is an unknown result.

15. The system of claim 1, wherein at least one of:

the first model-result category comprises an indication of a confidence level of the first model result;

or

the second model-result category comprises an indication of a confidence level of the second model result.

16. A method for managing application programming interfaces (API) comprising:

sending a first call to a first node-testing model associated with a first API;

receiving from the first node-testing model, a first model output based on the first call, the first model output comprising a first model result and a first model-result category;

receiving API call data associated with a plurality of APIs;

training a routing model to identify routing pathways between API nodes using the API call data;

sending the first model result to the routing model;

after sending the first model result, receiving, from the routing model, information identifying a second node-testing model associated with a second API;

sending a second call to the second node-testing model, the second call being based on the first model result;

receiving, from the second node-testing model, a second model output based on the second call, the second model output comprising a second model result and a second model-result category; and

performing at least one of;

generating an updated first node-testing model based on the first node-testing model when the first model result category comprises a failure or an unknown result;

generating an updated second node-testing model based on the second node-testing model when the second model result category comprises a failure or an unknown result;

generating, by a first translation model, an updated first call when the first model result category comprises a failure;

or

generating, by a second translation model, an updated second call when the second model result category comprises a failure.

17. A system for managing application programming interfaces (APIs), comprising:

one or more memory units storing instructions; and

one or more processors that execute the instructions to perform operations comprising;

receiving API call data associated with a plurality of APIs;

generating a plurality of node-testing models associated with the APIs;

sending a first call to a first one of the node-testing models;

receiving from the first node-testing model, a first model output based on the first call, the first model output comprising a first model result and a first model-result category;

receiving API call data associated with a plurality of APIs;

training a routing model to identify routing pathways between API nodes using the API call data;

sending the first model result to the routing model;

after sending the first model result, receiving, from the routing model, information identifying a second node-testing model associated with a second API; and

performing at least one of;

sending a second call to the second node-testing model, the second call being based on the first model result;

generating an updated first node-testing model based on the first node-testing model when the first model result category comprises at least one of a failure or an unknown result;

or

generating, by a translation model, an updated first call when the first model result category comprises a failure.