Auto-documentation for application program interfaces based on network requests and responses
Disclosed embodiments are directed at systems, methods, and architecture for providing auto-documentation to APIs. The auto documentation plugin is architecturally placed between an API and a client thereof and parses API requests and responses in order to generate auto-documentation. In some embodiments, the auto-documentation plugin is used to update preexisting documentation after updates. In some embodiments, the auto-documentation plugin accesses an online documentation repository. In some embodiments, the auto-documentation plugin makes use of a machine learning model to determine how and which portions of an existing documentation file to update.
1. A method comprising:
receiving, at a network node, one or more network packets containing an API request or API response associated with a first API;
proxying, by the network node, the API request or response;
parsing, by the network node, the API request or response;
determining, by the network node, a first behavioral indicator based on content of the API request or response as observed from said parsing;
determining, by the network node, that the first behavioral indicator is inconsistent with documentation for the first API based on a variance from a machine learning model; and
automatically updating, by the network node, the documentation for the first API based on the behavioral indicator.
2. The method of claim 1 , wherein the behavioral indicator is any of:
a title of a parameter;
a value provided for the parameter;
a sequence of requests and responses;
a host or endpoint identified in the API request or response; or
a protocol or language used.
3. The method of claim 1 , wherein the determination based on the machine learning model is further based on an observational history of a plurality of APIs.
4. The method of claim 1 , wherein the determination based on the machine learning model is further based on an observational history of a plurality of APIs having a sequence of API requests and responses.
5. The method of claim 1 , further comprising:
updating the machine learning model based on the content of the API request or response as observed from said parsing.
6. The method of claim 1 , wherein said determining inconsistency between the behavioral indicator and the documentation further comprises:
identifying that no documentation file exists for the first API; and
generating a documentation file for the first API.
7. The method of claim 1 , wherein said determining inconsistency between the behavioral indicator and the documentation is further based on a semantic analysis of the documentation and the content of the API request or response as observed from said parsing.
8. A method comprising:
receiving, at a gateway, an API request and an API response, wherein the API response corresponds to the API request, the API request and the API response associated with a first API;
determining, by the gateway, parameters of the API request and values of the API response that correspond to the parameters;
determining, by auto-documentation code, that any of the parameters or values are inconsistent with documentation for the first API based on a variance from a machine learning model; and
automatically updating by the auto-documentation code, the documentation for the first API based on any of the parameters or values are inconsistent with documentation for the first API.
9. The method of claim 8 , further comprising:
determining, by auto-documentation code, that any factors of:
a sequence of requests and responses including the API request and the API response;
a host or endpoint identified in the API request and the API response; or
a protocol or language used by the API request and an API response;
are inconsistent with documentation for the first API; and
automatically updating by the auto-documentation code, the documentation for the first API based on any of the factors that are inconsistent with documentation for the first API.
10. The method of claim 8 , wherein the determination based on the machine learning model is further based on an observational history of a plurality of APIs.
11. The method of claim 8 , wherein the determination based on the machine learning model is further based on an observational history of a plurality of APIs having a sequence of API requests and responses.
12. The method of claim 8 , further comprising:
updating the machine learning model based on the any of the parameters or values as observed.
13. The method of claim 8 , wherein said determining inconsistency between any of the parameters or values and the documentation further comprises:
identifying that no documentation file exists for the first API; and
generating a documentation file for the first API based on any of the parameters or values.
14. The method of claim 8 , wherein said determining inconsistency between the any of the parameters or values and the documentation is further based on a semantic analysis of the documentation and any of the parameters or values as observed.
15. A system comprising:
a network node within a network configured to receive one or more network packets containing an API request or API response associated with a first API, the network node further configured to proxy the API request or API response; and
memory containing auto-documentation code that when executed is configured to parse the API request or response and determine a first behavioral indicator based on content of the API request or response as parsed, and wherein the executed auto-documentation code is further configured to determine whether the parsed behavioral indicator is inconsistent with documentation for the first API based on a variance from a machine learning model within the memory, and when inconsistency is determined, automatically update the documentation for the first API based on the behavioral indicator.
16. The system of claim 15 , wherein the behavioral indicator is any of:
a title of a parameter; a value provided for the parameter;
a sequence of requests and responses;
a host or endpoint identified in the API request or response; or
a protocol or language used.
17. The system of claim 15 , wherein the determination based on the machine learning model is further based on an observational history of a plurality of APIs.
18. The system of claim 15 , wherein the determination based on the machine learning model is further based on an observational history of a plurality of APIs having a sequence of API requests and responses.
19. The system of claim 15 , wherein the executed auto-documentation code is further configured to update the machine learning model based on the content of the API request or response as observed.
20. The system of claim 15 , wherein the determination that the behavioral indicator and the documentation are inconsistent includes: an identification that no documentation file exists for the first API, and wherein the auto-documentation code is further configured to generate a documentation file for the first API.
21. The system of claim 15 , wherein the auto-documentation code determines the behavioral indicator and the documentation are inconsistent further based on a semantic analysis of the documentation and the content of the API request or response as observed.