@conference {apiace:2022:icws,
	title = {How Composable is the Web? An Empirical Study on OpenAPI Data model Compatibility},
	booktitle = {IEEE World Congress on Services (ICWS Symposium on Services for Machine Learning)},
	year = {2022},
	month = {July},
	pages = {415-424},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Barcelona, Spain},
	abstract = {Composing Web APIs is a widely adopted practice by developers to speed up the development process of complex Web applications, mashups, and data processing pipelines. However, since most publicly available APIs are built independently of each other, developers often need to invest their efforts in solving incompatibility issues by writing ad-hoc glue code, adapters and message translation mappings. How likely are Web APIs to be directly composable?

The paper presents an empirical study to determine the potential composability of a large collection of 20,587 public Web APIs by verifying their schemas{\textquoteright} compatibility. We define three levels of data model elements compatibility -- considering matches between property names and/or data types -- which can be determined statically based on API descriptions conforming to the OpenAPI specification. The study research questions address: to which extent are Web APIs compatible; the average number of compatible endpoints within each API; the likelihood of finding two APIs with at least one pair of compatible endpoints. 

To perform the analysis we developed a compatibility checker tool which can statically determine API schema compatibility on the three levels and find matching pairs of API responses which can be directly forwarded as requests to the same or other APIs. We run the tool on a dataset of 751,390 request and response message schemas extracted from publicly available OpenAPI descriptions.

The results indicate a relatively high number of compatible APIs when matching their data models only on the level of their elements{\textquoteright} data type. However, this number gets lower narrowing the scope to only the ones handling data objects having identical properties name. The average likelihood of finding two compatible APIs with both matching property names and data types reaches 21\%. Also, the number of compatible endpoints within the same API is very low.},
	keywords = {API Analytics, empirical study, mashups, OpenAPI, Web APIs},
	doi = {10.1109/ICWS55610.2022.00068},
	author = {Souhaila Serbout and Cesare Pautasso and Uwe Zdun}
}
@conference {2022:sose:apiace,
	title = {Impact of API Rate Limit on Reliability of Microservices- Based Architectures},
	booktitle = {16th International Conference on Service-Oriented System Engineering (SOSE 2022)},
	year = {2022},
	month = {August},
	publisher = {IEEE},
	organization = {IEEE},
	address = {San Francisco, USA},
	abstract = {Many API patterns and best practices have been
developed around microservices-based architectures, such as Rate
Limiting and Circuit Breaking, to increase quality properties
such as reliability, availability, scalability, and performance.
Even though estimates on such properties would be beneficial,
especially during the early design of such architectures, the real
impact of the patterns on these properties has not been rigorously
studied yet. This paper focuses on API Rate Limit and its impact
on reliability properties from the perspective of API clients. We
present an analytical model that considers specific workload
configurations and predefined rate limits and then accurately
predicts the success and failure rates of the back-end services.
The model also presents a method for adaptively fine-tuning
rate limits. We performed two extensive data experiments to
validate the model and measured Rate Limiting impacts, firstly
on a private cloud to minimize latency and other biases, and
secondly on the Google Cloud Platform to test our model in a
realistic cloud environment. In both experiments, we observed a
low percentage of prediction errors. Thus, we conclude that our
model can provide distributed system engineers and architects
with insights into an acceptable value for the rate limits to
choose for a given workload. Very few works empirically studied
the impact of Rate Limit or similar API-related patterns on
reliability.},
	keywords = {API Analytics},
	author = {Amine El Malki and Uwe Zdun and Cesare Pautasso}
}
