API Documentation Meetup 2016, London

Mar 6, 20163 likes403 views

Christian Heidenreich

In general, this presentation is about API Documentation plus a quick introduction to RAML and it's new version 1.0.

About me
API
Documenta.on
Meetup
2016,
London

•  European
Ci.zen

•  Technology
=
Love

•  Developer

•  Sporty
(at
least
some.mes)

•  Not
a
Brit

•  and
more
…

Chris.an
Vogel

Community
PM
/
Evangelist

MulesoO

@Chris.anVogel_

Remember?
API
Documenta.on
Meetup
2016,
London

Bo.om-‐up
API
Documenta.on
Meetup
2016,
London

Top-‐down
API
Documenta.on
Meetup
2016,
London

Speciﬁca(on

Today?
API
Documenta.on
Meetup
2016,
London

Pure

documents

BoRom

up

Top

down

Make Your Choice
API
Documenta.on
Meetup
2016,
London

and
more

RAML
API
Documenta.on
Meetup
2016,
London

A new open spec
for RESTful APIs
that's as clean
and as structured
as REST itself
RESTful API Modeling Language
the RAML Workgroup:
raml.org

RAML: Human Readable
API
Documenta.on
Meetup
2016,
London

Choose your format!
API
Documenta.on
Meetup
2016,
London

API

Console

Choose your format!
API
Documenta.on
Meetup
2016,
London

Slate

Choose your format!
API
Documenta.on
Meetup
2016,
London

HTML

PDF

Word

or
others

RAML

Write
once.

Use
many.

Crea.ve
laziness
encouraged.

RAML: Common Pa.erns
API
Documenta.on
Meetup
2016,
London

Resource Types
•  Collection
•  Collection Member
•  Document
Traits
•  Secure
•  Paged
Define Share Reuse

RAML: Common Pa.erns
API
Documenta.on
Meetup
2016,
London

resource type
schema
trait

Data
Models

complex
schemas
and
examples
become
simply
typed
models
that
can
be

used
for
code
genera.on,
clear
interpreta.on,
example
genera.on,
and

yes
–
even
mul.-‐format
schema
genera.on.

OR

API
Documenta.on
Meetup
2016,
London

Data
Models

data
models
are
easily
pulled
in
to

other
models,
used
as
schemas/

examples,
and
can
even
be
stored
in

libraries
and
namespaced!

API
Documenta.on
Meetup
2016,
London

Libraries

resourceTypes,
traits,
schemas,
examples,
data
types
(models),
and
more

can
all
be
stored
in
libraries,
which
can
then
be
pulled
into
the
RAML
spec

in
a
namespaced
format
–
leang
you
reuse
what
you
want,
and
ignore

what
you
don’t.

API
Documenta.on
Meetup
2016,
London

Overlays

your
API
doesn’t
just
live
in
one
environment,
so
neither
should
your

design,
documenta.on,
or
tests.

Overlays
let
you
“extend”
your
RAML

spec
for
your
development,
QA,
and
produc.on
environments.

Original
Spec
Overlay
seang
Produc.on
Values

API
Documenta.on
Meetup
2016,
London

Annota.ons

vendor
extensions
shouldn’t
be
a
pain,
and
in
RAML
–
they’re
just
“new”

proper.es
in
your
spec.

Plus
you
can
use
libraries

and
overlays
to
keep

annota.ons
separate

from
your
main
spec,

meaning
they
are
only

visible
to
the
vendor
you

want
using
them,
and

out
of
the
way
of
your

developers/
tech

writers/
CEO!

API
Documenta.on
Meetup
2016,
London

What else?
API
Documenta.on
Meetup
2016,
London

#
of
downloads

API
Workbench

13.7k

#
of
project

views

750k

RAML.org

APIWorkbench.com

@ramlapi

raml-‐org/raml-‐spec

API
Documenta.on
Meetup
2016,
London

This document discusses the challenges of API documentation and differences between technical authors and API writers. It outlines that API writers require both strong technical skills like coding abilities as well as writing skills, which can be difficult to find. It also notes differences in tools used for general documentation versus API documentation. Finally, it provides recommendations like improving skills through training, having realistic expectations of skills, improving standards and tools, and making documentation a shared team responsibility.

Moving into API documentation writingEllis Pratt

API documentation writing is a growing field as more applications use APIs. The role requires technical writing skills as well as strong domain knowledge of programming languages and tools. API documentation writers create reference materials that explain how developers can utilize APIs through documentation of resources, endpoints, parameters and code samples. While requiring technical skills, the role differs from traditional technical writing through its focus on reference content for other developers rather than task-based content for general audiences. Overall, API documentation writing presents opportunities for technical writers to expand their skills and participate in an important area of software development.

STC Summit 2015: API Documentation, an Example-Based ApproachLois Patterson

The document discusses best practices for documenting APIs. It recommends including clear documentation of an API's functionality through descriptions, tutorials, code examples, and testing environments. Good API documentation demonstrates consistency in naming conventions and clear explanations of errors. The document provides examples of APIs with well-designed documentation, such as Netflix, Twitter, and GitHub, and encourages making APIs as simple and intuitive as possible for developers and non-developers alike.

Publishing strategies for API documentationTom Johnson

Most of the common tools for publishing help material fall short when it comes to API documentation. Much API documentation (such as for Java, C++, or .NET APIs) is generated from comments in the source code. Their outputs don’t usually integrate with other help material, such as programming tutorials or scenario-based code samples. REST APIs are a breed of their own, with almost no standard tools for generating documentation from the source. The variety of outputs for REST APIs are as diverse as the APIs themselves, as you can see by browsing the 11,000+ web APIs on programmableweb.com. As a technical writer, what publishing strategies do you use for API documentation? Do you leave the reference material separate from the tutorials and code samples? Do you convert everything to DITA and merge it into a single output? Do you build your own help system from scratch that imports your REST API information? There’s not a one-size-fits-all approach. In this presentation, you’ll learn a variety of publishing strategies for different kinds of APIs, with examples of what works well for developer audiences. No matter what kind of API you’re working with, you’ll benefit from this survey of the API doc publishing scene. - See more at: http://idratherbewriting.com

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This document summarizes Tom Johnson's presentation on strategies for API documentation. It discusses different types of APIs like platform and REST APIs. For platform APIs, documentation is often generated from source code comments. REST API documentation should cover endpoints, parameters, response formats, and example calls. The document also summarizes a survey of API documentation practices. Popular publishing approaches include single page sites with code samples and interactive documentation. It raises questions for documentation writers to consider their technical skills and career paths.

REST Coder: Auto Generating Client Stubs and Documentation for REST APIsHiranya Jayathilaka

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Empathic API-DesignCorneil du Plessis

This document discusses API design and emphasizes the importance of empathy in API design. It provides considerations for designing easy to use and learn APIs. Examples of API documentation tools are also presented, including RAML, Swagger, Spring Rest Docs, and Javadoc. The document recommends that APIs be simple, unambiguous, usage-oriented, and easy to extend. It encourages API designers to consider their audience and ensure APIs are easy to use, even without documentation.

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Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora

Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.

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Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri

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Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi

Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185

GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise. https://arxiv.org/abs/2501.16495