Adaptive REST API Testing with Reinforcement Learning Article Swipe
Myeong‐Soo Kim
,
Saurabh Sinha
,
Alessandro Orso
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.04583
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.04583
Modern web services increasingly rely on REST APIs. Effectively testing these APIs is challenging due to the vast search space to be explored, which involves selecting API operations for sequence creation, choosing parameters for each operation from a potentially large set of parameters, and sampling values from the virtually infinite parameter input space. Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally (i.e., not considering their importance or complexity) and lacking prioritization strategies. Furthermore, these tools struggle when response schemas are absent in the specification or exhibit variants. To address these limitations, we present an adaptive REST API testing technique that incorporates reinforcement learning to prioritize operations and parameters during exploration. Our approach dynamically analyzes request and response data to inform dependent parameters and adopts a sampling-based strategy for efficient processing of dynamic API feedback. We evaluated our technique on ten RESTful services, comparing it against state-of-the-art REST testing tools with respect to code coverage achieved, requests generated, operations covered, and service failures triggered. Additionally, we performed an ablation study on prioritization, dynamic feedback analysis, and sampling to assess their individual effects. Our findings demonstrate that our approach outperforms existing REST API testing tools in terms of effectiveness, efficiency, and fault-finding ability.
Related Topics
Concepts
Computer science
Rest (music)
Reinforcement learning
Set (abstract data type)
Sampling (signal processing)
Machine learning
Key (lock)
Service (business)
Software engineering
Data mining
Artificial intelligence
Distributed computing
Programming language
Operating system
Computer vision
Cardiology
Filter (signal processing)
Economics
Medicine
Economy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.04583
- https://arxiv.org/pdf/2309.04583
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386644364
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https://openalex.org/W4386644364Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.04583Digital Object Identifier
- Title
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Adaptive REST API Testing with Reinforcement LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-08Full publication date if available
- Authors
-
Myeong‐Soo Kim, Saurabh Sinha, Alessandro OrsoList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.04583Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.04583Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.04583Direct OA link when available
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Computer science, Rest (music), Reinforcement learning, Set (abstract data type), Sampling (signal processing), Machine learning, Key (lock), Service (business), Software engineering, Data mining, Artificial intelligence, Distributed computing, Programming language, Operating system, Computer vision, Cardiology, Filter (signal processing), Economics, Medicine, EconomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.coverage | 158 |
| abstract_inverted_index.covered, | 163 |
| abstract_inverted_index.effects. | 185 |
| abstract_inverted_index.existing | 193 |
| abstract_inverted_index.failures | 166 |
| abstract_inverted_index.feedback | 177 |
| abstract_inverted_index.findings | 187 |
| abstract_inverted_index.infinite | 49 |
| abstract_inverted_index.involves | 24 |
| abstract_inverted_index.learning | 107 |
| abstract_inverted_index.requests | 160 |
| abstract_inverted_index.response | 82, 121 |
| abstract_inverted_index.sampling | 44, 180 |
| abstract_inverted_index.sequence | 29 |
| abstract_inverted_index.services | 2 |
| abstract_inverted_index.strategy | 131 |
| abstract_inverted_index.struggle | 80 |
| abstract_inverted_index.treating | 60 |
| abstract_inverted_index.achieved, | 159 |
| abstract_inverted_index.analysis, | 178 |
| abstract_inverted_index.comparing | 147 |
| abstract_inverted_index.creation, | 30 |
| abstract_inverted_index.dependent | 125 |
| abstract_inverted_index.efficient | 57, 133 |
| abstract_inverted_index.evaluated | 140 |
| abstract_inverted_index.explored, | 22 |
| abstract_inverted_index.feedback. | 138 |
| abstract_inverted_index.operation | 35 |
| abstract_inverted_index.parameter | 50 |
| abstract_inverted_index.performed | 170 |
| abstract_inverted_index.selecting | 25 |
| abstract_inverted_index.services, | 146 |
| abstract_inverted_index.technique | 103, 142 |
| abstract_inverted_index.variants. | 91 |
| abstract_inverted_index.virtually | 48 |
| abstract_inverted_index.generated, | 161 |
| abstract_inverted_index.importance | 70 |
| abstract_inverted_index.individual | 184 |
| abstract_inverted_index.operations | 27, 62, 110, 162 |
| abstract_inverted_index.parameters | 32, 64, 112, 126 |
| abstract_inverted_index.prioritize | 109 |
| abstract_inverted_index.processing | 134 |
| abstract_inverted_index.triggered. | 167 |
| abstract_inverted_index.Effectively | 8 |
| abstract_inverted_index.challenging | 13 |
| abstract_inverted_index.complexity) | 72 |
| abstract_inverted_index.considering | 68 |
| abstract_inverted_index.demonstrate | 188 |
| abstract_inverted_index.dynamically | 117 |
| abstract_inverted_index.efficiency, | 202 |
| abstract_inverted_index.exploration | 58 |
| abstract_inverted_index.mechanisms, | 59 |
| abstract_inverted_index.outperforms | 192 |
| abstract_inverted_index.parameters, | 42 |
| abstract_inverted_index.potentially | 38 |
| abstract_inverted_index.strategies. | 76 |
| abstract_inverted_index.Furthermore, | 77 |
| abstract_inverted_index.exploration. | 114 |
| abstract_inverted_index.incorporates | 105 |
| abstract_inverted_index.increasingly | 3 |
| abstract_inverted_index.limitations, | 95 |
| abstract_inverted_index.Additionally, | 168 |
| abstract_inverted_index.fault-finding | 204 |
| abstract_inverted_index.reinforcement | 106 |
| abstract_inverted_index.specification | 88 |
| abstract_inverted_index.effectiveness, | 201 |
| abstract_inverted_index.prioritization | 75 |
| abstract_inverted_index.sampling-based | 130 |
| abstract_inverted_index.prioritization, | 175 |
| abstract_inverted_index.state-of-the-art | 150 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |