Exploring Kusho.ai: A Real-World Testing Session and Competitive Comparison
As a software tester, I constantly look for tools that can enhance my productivity and ensure that the applications I test are of the highest quality. Recently, I had the opportunity to explore Kusho.ai, an AI-driven testing tool that promises to automate the creation of test cases for REST API-based applications. The tool is currently in its beta stage, so I approached it with an open mind, ready to uncover its strengths and weaknesses.
The Testing Scenario: Kusho.ai vs. Postman
For this testing session, I focused on a few key features of Kusho.ai:
I then compared my experience with Kusho.ai against a similar process in Postman, a widely used tool in the industry.
Setting Up the Test: Importing Postman Collections
To begin, I needed a real-world application to test. I used Datamaker, a web-based tool I developed that allows for the creation of random data samples based on a schema. The tool exposes a REST API for various operations, making it an ideal candidate for this testing session.
I prepared a Postman collection containing five API requests, ranging from retrieving namespaces to creating schema definitions. This collection was exported to JSON and then imported into Kusho.ai.
First Impressions: Scenario-Based Testing with Kusho.ai
Scenario-based testing is particularly effective when you understand the customer’s problem well. In this case, as someone who is both the developer and the tester, I was able to simulate a real user trying to solve a problem with Kusho.ai.
Upon importing the Postman collection, I was presented with a status page indicating that the import was processing. Surprisingly, Kusho.ai quickly generated several test suites, each named after one of the requests in the Postman collection. However, the issues began to surface as I explored further.
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Uncovering the Issues: Usability, Predictability, and Stability
The Competitive Edge: Time Savings and Experience
One of Kusho.ai’s key claims is that it can “generate exhaustive test suites for each API, saving hours of manual effort.” To test this, I recreated one of the test suites in Postman, which Kusho.ai had generated 15 test cases for. After removing duplicates, I recreated 10 cases in Postman, averaging 1.2 minutes per case.
Extrapolating this to the 79 test cases Kusho.ai generated, I would have spent just under 1.5 hours doing the same in Postman. While Kusho.ai does save time in test case creation, its claim of “exhaustive test suites” seems exaggerated based on my experience.
However, the experience of using Kusho.ai highlighted a significant difference in the depth of information provided by each tool. While Postman offered a rich, detailed view of responses (including headers and status codes), Kusho.ai’s simplified UI often hid critical details, leading to missed errors and misinterpretations.
Conclusion: Is Kusho.ai Worth It?
Despite its issues, Kusho.ai has potential, especially in its ability to save time in generating test cases. However, the tool is still in its early stages, and I encountered several severe problems that need addressing before it can be considered a reliable alternative to established tools like Postman.
For now, Kusho.ai may be a useful supplementary tool, especially for testers looking to quickly generate a large number of test cases. But for thorough testing and debugging, tools like Postman still hold the advantage with their rich feature sets and reliability.
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
6 个月Yo, looks like you're deep into the world of API testing with KushoAI! That's super cool. Have you explored using techniques like contract testing or mocking frameworks alongside KushoAI to ensure robust API behavior?