In an era where software systems are becoming increasingly critical, testing their functionality becomes paramount to deliver reliable and high-quality products. The concern becomes more pronounced when it comes to RESTful services, which are commonly documented using OpenAPI specifications. OpenAPI has two types of information embedded in its specifications: machine-readable and human-readable.
Machine-readable information has been extensively used for automated testing, but unfortunately, the human-readable part remains underused, despite it having the potential to significantly enhance the efficiency and effectiveness of test generation.
Let’s Talk About NLPtoREST
Understanding the necessity of harnessing the power of the human-readable part of OpenAPI, a new approach has been proposed – NLPtoREST—an automated method that uses natural language processing techniques to assist REST API testing. It operates by extracting additional OpenAPI rules from the human-readable part of the specification and enhancing the original specification accordingly.
How does it work?
The NLPtoREST methodology has been designed to be simple yet highly impactful. Given an API and its specification, it extracts extra OpenAPI rules from the human-readable segment and augments the original specification by adding these rules to it.
The already existing testing tools can then utilise the enhanced specification for smarter and better test case generation. However, it’s worth noting that rule extraction can sometimes be inaccurate due to the intrinsic ambiguity of natural language, or mismatches between documentation and performance. To counter this, NLPtoREST has integrated a validation process to eliminate any false rules.
Why does it matter?
The addition of the validation process and the ability to use the enhanced specifications in existing testing tools without modifications give NLPtoREST a substantial advantage over other API testing methods. It enables test generation tools to perform better, making it an invaluable tool for IT professionals.
Studies speak volumes
Empirical studies were conducted to assess the effectiveness of the rule extraction and validation approach and to understand the impact of the enhanced specifications on REST API testing tools. The results were quite promising. It was noticed that NLPtoREST could extract numerous relevant rules with high precision, which significantly improved the testing tools’ performance.
Wrapping up
API testing plays a critical role in ensuring the reliability of software systems. Novel approaches like the NLPtoREST, which cleverly combines natural language processing techniques to improve test case generation, can have a strongly positive impact on IT industry standards and performance.
Tags: #API #Testing #RestAPI #OpenAPI #NLPtoREST