AI模型GPT: 用于企业软件交付的生成式AI | 好奇软件

Table of Contents

1. Introduction
2. Overview of Modeler GPT
3. Login Requirement
   • Validation of user credentials
   • Error message for incorrect credentials
4. Adding a Feedback Loop
   • Using the AI model as a chat environment
   • Incorporating enhancements and feedback
5. Refining the Model
   • Introducing a capture requirement
   • Verifying the capture for valid credentials
6. Adding Descriptions and Expected Results
   • Modifying the model to include detailed steps
   • Specifying the expected results for each Scenario
7. Generating Test Cases
   • Generating different scenarios using the AI model
   • Pushing test cases to a test case management tool (Excel)
8. Advanced Example: Drinks Promotion
   • Minimum mile requirement for a free drink
   • Tracking miles and presenting results for promotion
   • Time validity and selected drink eligibility
9. Automation Example: Searching on Google
   • Creating a model for searching on Google
   • Adding automation for navigation and search query
10. Self-Improvement of the Model
    • Evaluating and improving the model's suggestions
    • Enhancements for failed transactions and errors
11. Conclusion

Introduction

In this article, we will explore Modeler GPT, a powerful software feature offered by Curiosity. Modeler GPT allows users to easily convert text into functional tests within minutes. We will Delve into various aspects of Modeler GPT, starting from login requirements to more advanced use cases like automation and self-improvement. Each section will provide a detailed examination of the topic, including practical examples and real-world applications.

1. Overview of Modeler GPT

Before diving into the specifics, it is important to understand the basic features and functionality of Modeler GPT. This section will provide an overview of Modeler GPT, explaining its purpose and capabilities. We will also explore the benefits of using Modeler GPT in software testing processes.

2. Login Requirement

The login requirement is a crucial aspect of any software application that requires user authentication. In this section, we will discuss the login process and how Modeler GPT can assist in creating test models for this requirement. We will examine the steps involved, such as validating user credentials and displaying error messages for incorrect login attempts.

3. Adding a Feedback Loop

To enhance the accuracy and efficiency of our test models, we can introduce a feedback loop in the interaction with Modeler GPT. This section will explain how to utilize the AI model as a chat environment, allowing users to provide feedback and make enhancements to the model. We will explore the benefits of leveraging user input to refine and optimize the test models.

4. Refining the Model

Building upon the previous section's feedback loop, we will now focus on refining the test models using specific requirements. We will demonstrate this process by incorporating a capture requirement, which adds an additional layer of security for user authentication. By verifying the capture input, we can ensure that only users with valid credentials can proceed.

5. Adding Descriptions and Expected Results

In this section, we will further enhance our test models by adding detailed descriptions and specifying expected results. This step helps Create comprehensive and understandable test scenarios. By providing clear instructions and defining the expected outcomes, we can effectively communicate the intended behavior of the system being tested.

6. Generating Test Cases

Once we have refined and enriched our test models, we can generate test cases Based on the different scenarios. This section will demonstrate how Modeler GPT can automatically generate test cases. We'll explore the process of generating multiple test scenarios and discuss ways to export and manage these test cases using industry-standard tools, such as a test case management tool like Excel.

7. Advanced Example: Drinks Promotion

Moving beyond basic login requirements, we will now explore a more advanced example of using Modeler GPT. We will focus on a drinks promotion scenario that imposes specific conditions for customers to qualify for a free drink. This section will examine the requirements related to minimum distance tracking, presenting results to cashiers, time validity, and drink eligibility criteria.

8. Automation Example: Searching on Google

Automation is a crucial aspect of software testing, as it allows for efficient execution of repetitive tasks. In this section, we will explore how Modeler GPT can be utilized for automating actions on websites. We will specifically focus on searching on Google as an example, demonstrating how Modeler GPT can create models capable of navigating to a website, entering search queries, and submitting them.

9. Self-Improvement of the Model

Modeler GPT has the capability to evaluate and improve itself based on user feedback. In this section, we will explore the self-improvement feature of Modeler GPT. We will discuss how the AI model evaluates its own performance and provides suggestions for improvement. Specific examples of enhancements for failed transactions, insufficient funds, and network errors will be provided.

10. Conclusion

In the final section, we will summarize the key points discussed throughout the article. We will highlight the benefits and capabilities of Modeler GPT in software testing processes. Additionally, we will provide insights into the future developments and advancements in AI-driven testing technologies.

Article

1. Overview of Modeler GPT

Modeler GPT, developed by Curiosity, is a cutting-edge software feature designed to assist in converting text into functional tests rapidly. With Modeler GPT, users can bridge the gap between textual requirements and actual test models in a matter of minutes. By leveraging the power of Generative AI, Modeler GPT offers an intuitive and efficient approach to software testing. This innovative tool enables users to create test models accurately, saving valuable time and effort in the testing process.

Modeler GPT provides an array of benefits for software testing. It streamlines the test creation process, eliminating the need for manual test case development from scratch. With just a simple prompt, Modeler GPT generates a model that can be readily used for testing. This feature significantly reduces the time and resources required for test case preparation. Furthermore, Modeler GPT enhances collaboration among stakeholders by providing a chat environment where suggestions and feedback can be incorporated seamlessly. This ensures that the test models created accurately reflect the desired system behavior.

2. Login Requirement

One common requirement in software applications is the need for user authentication through a login process. Modeler GPT assists in creating test models for this essential login requirement, ensuring the smooth functioning of the authentication process. By utilizing the generative AI capabilities of Modeler GPT, users can easily create models that validate user credentials and handle error scenarios effectively.

To begin, users can prompt Modeler GPT by providing a login requirement. The AI model will then analyze the prompt and generate a simple model that represents the login process. The model includes steps such as entering a valid username, entering a valid password, and clicking the submit button. Once the user's credentials are validated, the model logs them in. However, if the credentials are incorrect, an error message is displayed, indicating an invalid login attempt.

The login requirement feature in Modeler GPT streamlines the creation of test models for login processes. It ensures comprehensive test coverage by considering both valid and invalid scenarios. By leveraging the generative AI capabilities, Modeler GPT simplifies the testing process and improves efficiency.

3. Adding a Feedback Loop

Modeler GPT introduces a unique feedback loop feature, allowing users to enhance and refine their test models through a chat environment. This feedback loop enables users to collaborate with the AI model, providing insights, suggestions, and improvements. By actively engaging in this iterative process, users can optimize their test models and ensure the accuracy and reliability of the generated tests.

The feedback loop starts by using the AI model as a chat environment. Users can post enhancements and feedback to the AI, helping to fine-tune the generated test models. This collaborative approach fosters communication and fosters mutual understanding between the user and the AI, resulting in improved test models.

Through the feedback loop, users can Interact with the AI model, suggesting modifications or refinements to meet specific requirements. For example, users can include additional steps, such as a capture requirement, to further enhance security. By incorporating user feedback, Modeler GPT adapts and improves the test models, ensuring they accurately reflect the desired system behavior.

The feedback loop feature of Modeler GPT promotes an iterative and collaborative approach to test model development. This dynamic interaction between users and the AI model enhances the accuracy and effectiveness of the generated tests.

4. Refining the Model

Building upon the feedback loop, users can further refine their test models using specific requirements. Modeler GPT allows for the introduction of additional steps or conditions to create more comprehensive and realistic test scenarios. This section will explore the process of refining the model by incorporating capture requirements for enhanced security in user authentication.

By adding capture requirements, the login process becomes more secure as users need to verify their identities through additional steps. Modeler GPT assists in seamlessly integrating the capture requirement into the test models. The AI model generates a refined version of the test model, where the user is prompted to enter a capture in addition to a valid username and password.

When the capture is entered correctly, the model verifies it and allows the user to log in. However, if an incorrect capture is entered, an error message is displayed, preventing unauthorized access. The incorporation of capture requirements adds an extra layer of security to the login process, ensuring that only users with valid credentials can proceed.

Refining the test model by incorporating capture requirements showcases the flexibility and adaptability of Modeler GPT. Users can easily modify the test models to meet specific security needs and ensure comprehensive testing coverage.

5. Adding Descriptions and Expected Results

To create comprehensive test models, it is essential to provide detailed descriptions and specify the expected results for each step. Modeler GPT enables users to enhance their test models by adding clear instructions and expectations, facilitating effective test execution and analysis.

By prompting Modeler GPT to include descriptions and expected results, users can generate test models with precise instructions for each step. Instead of simple Prompts, the AI model generates detailed descriptions, specifying actions such as entering a username, entering a registered password, and clicking the submit button. Furthermore, the expected results for each scenario are explicitly defined, outlining the successful login or the occurrence of an error.

The addition of descriptions and expected results contributes to better test coverage, as the models capture the intended behavior of the system. Test execution becomes more efficient, as testers can refer to the detailed instructions throughout the testing process. Moreover, the specified expected results allow for easy comparison and analysis of the actual system behavior with the desired outcomes.

Modeler GPT's capability to incorporate descriptions and expected results enhances the Clarity and reliability of the generated test models. By providing comprehensive guidance, users can effectively execute and evaluate the tests, ensuring accurate system validation.

6. Generating Test Cases

Once the test models are refined and enriched, users can generate test cases based on various scenarios. Modeler GPT allows for the automatic generation of multiple test cases, enabling comprehensive coverage and efficient test execution.

By leveraging the generative AI capabilities of Modeler GPT, users can easily generate different test scenarios based on their refined models. The AI model analyzes the potential inputs and outputs, generating a variety of test cases that cover a wide range of conditions. These test cases provide extensive coverage, ensuring that the system is thoroughly tested.

Once the test cases are generated, users can export them to a test case management tool, such as Excel, for better organization and tracking. The generated test cases include detailed descriptions and expected results, facilitating efficient execution and analysis.

Modeler GPT's ability to generate test cases automates the tedious process of manual test case creation. By dynamically generating test scenarios, testers can focus on executing the tests and analyzing the results, improving efficiency and productivity.

7. Advanced Example: Drinks Promotion

To showcase the versatility of Modeler GPT, we will explore an advanced example of using the tool for a drinks promotion scenario. This example introduces specific conditions for customers to qualify for a free drink. Let's examine the requirements in Detail.

The drinks promotion requires customers to run a minimum of 10 miles per week to be eligible for a free drink. Customers must track their miles and present the results to the cashier to receive the promotion. The promotion is valid for a limited time period and is only applicable to selected drinks determined by the establishment. Additionally, customers must be 18 years old or older to participate in the promotion.

With Modeler GPT, users can easily create test models that simulate the entire drinks promotion process. The test models can include steps such as tracking miles, presenting the results to the cashier, verifying eligibility, and applying the promotion to selected drinks. By generating these test models, testers can ensure that the promotion is accurately implemented and that all the specified conditions are met.

The advanced example of the drinks promotion demonstrates the versatility of Modeler GPT in handling complex scenarios. By modeling intricate requirements, users can execute comprehensive tests that cover all aspects of the promotion.

8. Automation Example: Searching on Google

Automation is an integral part of software testing, enabling efficient execution of repetitive tasks. Modeler GPT can be leveraged to create models for automating actions on websites. In this example, we will focus on searching on Google as an automation scenario.

With Modeler GPT, users can create a model that navigates to the Google site, enters a search query, and submits it. By generating an automation model, testers can streamline the process of searching on Google. The generated model contains the necessary automation code and identifiers to perform each action. This model can then be used to create and execute automation scripts, saving time and effort.

Automation scenarios, like searching on Google, demonstrates the practical application of Modeler GPT in streamlining repetitive tasks. By utilizing the power of automation, testers can focus on more critical aspects of the testing process, improving efficiency and productivity.

9. Self-Improvement of the Model

Modeler GPT introduces a powerful self-improvement feature, allowing the AI model to evaluate itself and provide suggestions for refinement. Through self-evaluation, the AI model identifies areas for improvement and suggests enhancements based on the specific requirements.

Once a model is created, users can prompt Modeler GPT to evaluate itself and suggest improvements. The AI model analyzes the model's performance and provides suggestions for specific scenarios, such as failed transactions, insufficient funds, and network errors. By incorporating these suggestions, users can enhance the test models to handle such scenarios more effectively.

The self-improvement feature further enhances the accuracy and reliability of the generated test models. By leveraging the AI model's self-evaluation capabilities, users can continuously refine the test models and adapt to changing requirements.

10. Conclusion

Modeler GPT from Curiosity offers a revolutionary approach to rapid test model creation. This article explored various aspects of Modeler GPT, starting from login requirements to advanced use cases like automation and self-improvement. By leveraging the power of generative AI, Modeler GPT streamlines the software testing process, enhances collaboration, facilitates iteration, and improves efficiency.

Modeler GPT empowers testers and developers to create accurate and comprehensive test models that precisely reflect the desired system behavior. By incorporating feedback loops, refining the models, adding descriptions and expected results, generating test cases, and automating repetitive tasks, testers can execute high-quality tests with ease.

As AI-driven testing technologies Continue to advance, Modeler GPT remains at the forefront, providing innovative solutions to complex testing challenges. With the ability to self-improve and adapt, Modeler GPT ensures that testing processes stay Relevant and effective in a dynamic technological landscape.

Explore the power of Modeler GPT and revolutionize your software testing approach today!

FAQ

Q: What is Modeler GPT?

A: Modeler GPT is a software feature developed by Curiosity that enables the rapid conversion of text into functional tests. It leverages generative AI to facilitate the creation of accurate and comprehensive test models quickly.

Q: How does Modeler GPT assist in the login requirement?

A: Modeler GPT simplifies the creation of test models for the login requirement by generating models that validate user credentials and handle error scenarios effectively. It streamlines the testing process and improves efficiency.

Q: How can Modeler GPT be used to automate tasks?

A: Modeler GPT allows for the creation of automation models, streamlining repetitive tasks. By generating automation scripts for actions like searching on Google, testers can save time and effort in the testing process.

Q: Can Modeler GPT generate test cases?

A: Yes, Modeler GPT can generate test cases based on the refined test models. It provides comprehensive test coverage by generating multiple scenarios, facilitating thorough testing.

Q: How does Modeler GPT self-improve?

A: Modeler GPT evaluates itself and provides suggestions for refinement based on specific requirements. It analyzes its own performance and suggests enhancements for scenarios like failed transactions or network errors, improving the accuracy of the test models.