Leveraging Large Language Models to Improve REST API Testing Article Swipe
Myeong‐Soo Kim
,
Tyler Stennett
,
Dhruv Shah
,
Saurabh Sinha
,
Alessandro Orso
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.00894
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.00894
The widespread adoption of REST APIs, coupled with their growing complexity and size, has led to the need for automated REST API testing tools. Current tools focus on the structured data in REST API specifications but often neglect valuable insights available in unstructured natural-language descriptions in the specifications, which leads to suboptimal test coverage. Recently, to address this gap, researchers have developed techniques that extract rules from these human-readable descriptions and query knowledge bases to derive meaningful input values. However, these techniques are limited in the types of rules they can extract and prone to produce inaccurate results. This paper presents RESTGPT, an innovative approach that leverages the power and intrinsic context-awareness of Large Language Models (LLMs) to improve REST API testing. RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification. It then augments the original specification with these rules and values. Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation. Given these promising results, we outline future research directions for advancing REST API testing through LLMs.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.00894
- https://arxiv.org/pdf/2312.00894
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389362580
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389362580Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2312.00894Digital Object Identifier
- Title
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Leveraging Large Language Models to Improve REST API TestingWork 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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2023-12-01Full publication date if available
- Authors
-
Myeong‐Soo Kim, Tyler Stennett, Dhruv Shah, Saurabh Sinha, Alessandro OrsoList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.00894Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.00894Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2312.00894Direct OA link when available
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Computer science, Rest (music), Context (archaeology), Natural language, Fuzz testing, Artificial intelligence, Programming language, Machine learning, Software engineering, Natural language processing, Data mining, Data science, Software, Medicine, Biology, Cardiology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.valuable | 38 |
| abstract_inverted_index.Recently, | 54 |
| abstract_inverted_index.advancing | 179 |
| abstract_inverted_index.automated | 19 |
| abstract_inverted_index.available | 40 |
| abstract_inverted_index.coverage. | 53 |
| abstract_inverted_index.developed | 61 |
| abstract_inverted_index.generates | 133 |
| abstract_inverted_index.intrinsic | 110 |
| abstract_inverted_index.knowledge | 72 |
| abstract_inverted_index.leverages | 106 |
| abstract_inverted_index.parameter | 135 |
| abstract_inverted_index.promising | 171 |
| abstract_inverted_index.complexity | 10 |
| abstract_inverted_index.directions | 177 |
| abstract_inverted_index.extraction | 165 |
| abstract_inverted_index.inaccurate | 96 |
| abstract_inverted_index.innovative | 103 |
| abstract_inverted_index.meaningful | 76 |
| abstract_inverted_index.structured | 29 |
| abstract_inverted_index.suboptimal | 51 |
| abstract_inverted_index.techniques | 62, 81, 161 |
| abstract_inverted_index.widespread | 1 |
| abstract_inverted_index.evaluations | 155 |
| abstract_inverted_index.generation. | 168 |
| abstract_inverted_index.outperforms | 159 |
| abstract_inverted_index.researchers | 59 |
| abstract_inverted_index.descriptions | 44, 69, 139 |
| abstract_inverted_index.unstructured | 42 |
| abstract_inverted_index.specification | 148 |
| abstract_inverted_index.human-readable | 68 |
| abstract_inverted_index.specification, | 128 |
| abstract_inverted_index.specification. | 142 |
| abstract_inverted_index.specifications | 34 |
| abstract_inverted_index.specifications, | 47 |
| abstract_inverted_index.natural-language | 43, 138 |
| abstract_inverted_index.context-awareness | 111 |
| abstract_inverted_index.machine-interpretable | 130 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |