What Are The Pain Points?
Environment Differences: Tests often fail when moving between local, staging, and production environments due to setup discrepancies.
Complex Negative Testing: Testing complex sequences, such as HTTP 200 followed by 407 for the same resource, is tricky.
Updating Tests: Changes in the application often necessitate widespread updates to existing tests.
Inaccurate Self-Healing Claims: Self-healing mechanisms often fail to correct issues automatically as claimed, leading to increased manual intervention and oversight.
Scalability of No-Code/Low-Code UI Tests: While no-code/low-code tools simplify creating a few UI tests with clicks and drag-and-drop actions, scaling this to hundreds or thousands of tests becomes impractical and time-consuming.
Automation vs. Deadline Trade-offs: Time allocated for automating tests often takes a hit during sprint planning due to pressing deadlines, leading to more manual testing and increased risk of future regressions.
Flaky Environment: Unstable dependencies can cause inconsistent test results.
Limited Resources: Access to necessary databases and services is restricted, often due to compliance requirements like SOC2 that mandate a VPN.
Our mission
To reinvent testing into an assertion-free, near zero-effort process by removing the traditional need to write test assertions and leveraging AI/ML to streamline everything.
How Testing Differs from Application Code?
Testing involves sending requests and checking responses. Unlike ongoing tasks like microservices or batch jobs, testing is quick and doesn't run continuously. This makes it ideal for AI/ML automation because the finite and predictable nature of testing tasks allows AI algorithms to efficiently learn and optimize test cases without the complexity of continuous monitoring.
Traditional programming languages like Java, Python, or JavaScript are powerful, but they often add unnecessary complexity to testing, which doesn't require continuous code execution or handle complex logic like multi-threading and conditional operations.
Tesmon's Approach
We reimagined testing from the ground up. Tesmon Desktop integrates seamlessly into the development cycle, making it feel like testing isn't even happening. Here's how we do it:
- Interactive Local Testing: Use Tesmon Desktop locally to interact with your APIs, databases, caches, Kafka, and more, while Tesmon continues to learn and autonomously create tests.
- Continuous Learning and Adaptation: Our AI/ML pipelines train models to generate deterministic, repeatable tests automatically, eliminating the need for manual assertions.
- Automatic Test Updates: Tesmon adapts to changes in your system automatically. It notifies you of changes, and with a single click, you can confirm these updates, teaching Tesmon to integrate them seamlessly.
- Lifecycle Extension with Tesmon Cloud: Tesmon Cloud extends the capabilities developed on the Desktop, covering the entire lifecycle from local development through staging to production.
Yet Another AI Demo?
While many AI demos appear flawless in promotional videos, they frequently underperform in practical settings. However, see for yourself with Assertion-Free Test Creation using Tesmon Tesbot.
Watch and observe the performance firsthand. To get started, download Tesmon Desktop: Download Now.
Transform Your Testing Experience
Create & Run Your First Test Case in Under 30 Seconds
Experience effortless testing with Tesmon, designed to integrate so smoothly into your development process that you'll hardly notice it's there.
Developer Documentation: Explore our detailed Developer Docs to get started.
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