Automating SLA-Driven API Development with SLA4OAI

Gamez-Diaz, Antonio; Fernández, Pablo; Ruiz–Cortés, Antonio

doi:10.1007/978-3-030-33702-5_2

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…This will allow us to perform a more extensive evaluation of the framework. On the other hand, we intend to make RESTest SLA-aware with SLA4OAI [6], so that it can be deployed in API gateways such as Governify [7] and perform autonomous functional and non-functional testing of microservices architectures.…”

Section: Discussionmentioning

confidence: 99%

RESTest: Black-Box Constraint-Based Testing of RESTful Web APIs

Martin-Lopez

Segura

Ruiz–Cortés

2020

Lecture Notes in Computer Science

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Automated testing approaches for RESTful web APIs typically follow a black-box strategy, where test cases are derived from the API specification. These techniques show promising results, but they neglect constraints among input parameters (so-called inter-parameter dependencies), as these cannot be formally described in current API specification languages. As a result, black-box tools rely on brute force to generate valid test cases, i.e., those satisfying all the input constraints. This is not only extremely inefficient, but it is also unlikely to work for most real-world services, where inter-parameter dependencies are complex and pervasive. In this paper, we present RESTest, a framework for automated black-box testing of RESTful APIs. Among its key features, RESTest supports the specification and automated analysis of interparameter dependencies, enabling the use of constraint solvers for the automated generation of valid test cases. This allows to detect more faults, and faster, through a deeper evaluation of valid and invalid input parameters' combinations and the use of novel test oracles. Evaluation results on 6 commercial APIs show that RESTest can efficiently generate up to 99% more valid test cases than random testing techniques, 60% on average. More importantly, RESTest revealed 2K failures undetected by random testing, uncovering bugs in all the services under test.

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Section: Discussionmentioning

confidence: 99%

RESTest: Black-Box Constraint-Based Testing of RESTful Web APIs

Martin-Lopez

Segura

Ruiz–Cortés

2020

Lecture Notes in Computer Science

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“…We also aim to automate metamorphic testing of RESTful APIs [25] based on existing metamorphic relation patterns for query-based systems [24]. On the other hand, we intend to support non-functional testing of RESTful APIs by leveraging existing specifications such as SLA4OAI [17]. In doing so, we aim to make RESTest a full-fledged framework for online testing and monitoring of RESTful APIs.…”

Section: Discussionmentioning

confidence: 99%

RESTest: automated black-box testing of RESTful web APIs

Martin-Lopez

Segura

Ruiz–Cortés

2021

Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis

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Testing RESTful APIs thoroughly is critical due to their key role in software integration. Existing tools for the automated generation of test cases in this domain have shown great promise, but their applicability is limited as they mostly rely on random inputs, i.e., fuzzing. In this paper, we present RESTest, an open source blackbox testing framework for RESTful web APIs. Based on the API specification, RESTest supports the generation of test cases using different testing techniques such as fuzzing and constraint-based testing, among others. RESTest is developed as a framework and can be easily extended with new test case generators and test writers for different programming languages. We evaluate the tool in two scenarios: offline and online testing. In the former, we show how RESTest can efficiently generate realistic test cases (test inputs and test oracles) that uncover bugs in real-world APIs. In the latter, we show RESTest's capabilities as a continuous testing and monitoring framework. Demo video: https://youtu.be/1f_tjdkaCKo.

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“…ML service contracts: Service contracts have been researched intensively, e.g., [29], [30], [31], [32], but they do not support ML-specific attributes. In [4], important nonfunctional attributes for ML and their research questions are discussed.…”

Section: Related Workmentioning

confidence: 99%

QoA4ML - A Framework for Supporting Contracts in Machine Learning Services

Truong

Nguyen

2021

2021 IEEE International Conference on Web Services (ICWS)

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Important service-level constraints in machine learning (ML) services must be communicated and agreed among relevant stakeholders. Due to the lack of studies and support, it is unclear which and how ML-specific attributes and constraints should be specified and assured in service contracts for ML services. This paper examines service contracts in the three stakeholders engagement model of ML services. We identify key ML-specific attributes that should be specified and monitored for the ML service provider, ML consumer and ML infrastructure provider. Based on that, we propose QoA4ML (Quality of Analytics for ML) as a framework to support ML-specific service contracts. QoA4ML includes an ML-specific service contract specification, monitoring utilities and a contract observability service. To illustrate the usefulness of QoA4ML, we present real-world examples for contract terms and policies, monitoring and contract evaluation with dynamic ML services in predictive maintenance.

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Automating SLA-Driven API Development with SLA4OAI

Cited by 12 publications

References 15 publications

RESTest: Black-Box Constraint-Based Testing of RESTful Web APIs

RESTest: Black-Box Constraint-Based Testing of RESTful Web APIs

RESTest: automated black-box testing of RESTful web APIs

QoA4ML - A Framework for Supporting Contracts in Machine Learning Services

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