selenium

Selenium Script Generation Made Easy with ChatGPT Plugin

by with No Comment AI, Quality Engineering

Selenium Script Generation Made Easy with ChatGPT Plugin

  • AI, Quality Engineering
  • July 5, 2023

ChatGPT has revolutionized test automation. One notable example is the generation of Selenium script, which has become significantly easier.

Setting up ChatGPT plugin

Visit the ChatGPT website and download the plugin for your code editor or IDE of your choice. Once installed, configure the plugin by providing your ChatGPT API key or authentication details. You can obtain an API key from the ChatGPT website. This key will enable your code editor to communicate with the ChatGPT model. After configuring the plugin, establish a connection to the ChatGPT model. This step might require an internet connection. Once connected, you can start leveraging the power of ChatGPT within your code editor.

Give ‘Pre-conditions’ and ‘Test conditions’ as input to ChatGPT

Gather the data that you want to use to populate the “Pre-conditions” and “Test conditions” columns in your Excel file. When using a data-driven framework, you can leverage ChatGPT by providing it with data from an Excel file.

A data-driven framework is an approach to software testing where test cases are created based on data inputs and expected outputs. By utilizing this framework, you can organize your test cases in a structured manner and easily manage large sets of data.

Pre-conditions:
These are the initial conditions or setup required before executing a test case. They define the state of the system or application under test before the test scenario is executed. Pre-conditions can include things like:

Data or configurations that need to be present

The system or application is in a specific state

Any prerequisites or dependencies that need to be satisfied

Test conditions:
These are the specific conditions or inputs that you want to test in a given scenario. Test conditions are the actions or events that you want to examine during the testing process. They could include:

User interactions or operations

Boundary cases or edge conditions

Different input combinations or scenarios

Call the ChatGPT API and send “pre-conditions” and “test conditions” as inputs to interact with the ChatGPT model. The ChatGPT API will process the input and generate a response based on the provided data.

Create an ArrayList

Create an ArrayList object to store the generated test case steps for selenium script generation. Each test case step should be represented as a string.

In the API request body, you will pass the ‘Test case steps array list’ as input to ChatGPT. Depending on the API requirements, you may need to serialize the array list. Upon successfully sending the API request, you will receive a response from the ChatGPT API. The response will contain the generated Selenium script based on the provided ‘Array list’. Declare and initialize the ArrayList to store the generated Selenium script.

Save the Generated ‘Selenium script’

Once you have the generated Selenium script, add it to the Array List. If you want to generate multiple scripts, repeat the same with different prompts or test case steps. Each generated script can be stored in the ArrayList.

Run the generated ‘Selenium script’

Review the generated script and customize it as needed. Launch the PyCharm Integrated Development Environment (IDE) on your computer. Open PyCharm IDE and create a new Python project by selecting “File” -> “New Project.” Choose a name and location for your project and set up the project environment. Before running the generated Selenium script, you need to install the Selenium library. Before running the script, you need to configure the WebDriver to use a specific browser. For example, if you want to use Google Chrome, you need to download the corresponding ChromeDriver executable and provide its path in your script.

Now you are ready to run the Selenium script. Right-click on the Python file containing the script in the project pane and select “Run” or use the keyboard shortcut. PyCharm will execute the script, and the browser controlled by Selenium will perform the actions described in the script.

Conclusion

A notable advantage of the ChatGPT Plugin is its adaptability and extensibility. It can be easily customized to support various programming languages, frameworks, and testing scenarios. By automating the script generation process, users can experience a remarkable increase in productivity, with reported improvements ranging from 40% to 60%, depending on the complexity of the project and the expertise of the user.

URC Construction selects Trivecta Digital as its Zoho People Implementation partner

by with No Comment News

URC Construction selects Trivecta Digital as its Zoho People Implementation partner

Chennai, India, 5 July 2023 – Trivecta Digital, a leading Information Technology solutions provider and Zoho Partner, announces that URC Construction, a leading infrastructure company, has selected Trivecta as its Zoho People implementation partner.

URC Construction has established a reputation for delivering large-scale and complex infrastructure projects for over 66 years and has a workforce of more than 2500 employees.  As the company continues to expand and take on more challenging ventures,  managing its large and diverse workforce, geographically distributed across multiple sites, and scaling human resources processes have also become increasingly challenging.

Choosing Zoho People as the HR management software brings numerous benefits to URC Construction. From time tracking and leave management to performance appraisal and employee self-service, Zoho People provides a centralized platform that streamlines HR processes and enhances overall organizational efficiency.

Trivecta Digital’s expertise in Zoho People ensures a customized implementation that aligns perfectly with URC Construction’s requirements. Their team of experts is committed to ensuring a smooth transition, training, and support to maximize the value of Zoho People for URC Construction.

Commenting on the successful collaboration, Paramasivam Venkataramasamy, CTO of Trivecta Digital, said, “I am thrilled about our collaboration with URC Construction as a Zoho People implementation partner. Infrastructure companies, such as URC Construction, operate in a dynamic and demanding industry characterized by manual processes and offline data. By implementing Zoho People, we will help URC Construction eliminate manual processes, and gain real time visibility into all critical human resource metrics and costs.” 

About URC Construction:

For over 6 decades, URC Construction has been delivering custom-designed turnkey projects to government agencies, large corporations and private organizations with a track record of completing 1500 projects.With a workforce of more than 2500 employees, URC Construction has established itself as a prominent player in the industry.

About Zoho People: 

Zoho People, a cloud-based HR software, is designed to support the growth and development of employees,  automate HR operations and cultivate a high-performing workforce, focusing on prioritizing the employee experience.

About Trivecta Digital:

Trivecta Digital is an information technology solutions and services provider specializing in Zoho consulting services. With a dedicated team of experts, Trivecta Digital leverages its extensive knowledge and experience to implement and customize Zoho’s suite of applications, enabling organizations to streamline operations and increase efficiency.

For more information, contact info@trivectadigital.com

Exploring-the-Role-of-AI-in-Achieving-Comprehensive-Test-Coverage

Exploring the Role of AI in Achieving Comprehensive Test Coverage

by with No Comment AI, Quality Engineering

Exploring the Role of AI in Achieving Comprehensive Test Coverage

  • AI, Quality Engineering
  • June 6, 2023

Software applications are rapidly growing in complexity and becoming more dynamic by adapting to the evolving needs of users. As a consequence, achieving comprehensive test coverage is becoming more challenging with traditional processes and resources being stretched to the limits.
With Artificial Intelligence (AI) already revolutionizing many aspects of application development and testing, we explore the role of AI in achieving more efficient and effective test coverage.

Automated Test Case Generation
AI-based tools can automatically generate test cases by leveraging historic test data for functionalities with a defined set of expected behaviors. In addition to leveraging the above inputs, these algorithms help analyze code, identify potential test scenarios and generate inputs that expand test coverage, and reduce the risk of missing critical test scenarios.
Here is a recent use case: Sapienz, an AI-powered tool by Facebook, is designed to generate test cases by analyzing various application components including features, buttons, menus, and screens to understand their structure and behavior. Sapienz tests the application’s responsiveness and behavior under different scenarios by simulating user interactions like tapping, scrolling, and navigating. It generates diverse test scenarios and test cases, covering multiple paths and functionalities to identify potential issues and bugs.

Intelligent Test Case Prioritization
AI-driven test prioritization optimizes software testing by leveraging AI algorithms to analyze bug history, code changes, and customer feedback. By considering these factors, algorithms can identify areas of the software that are more prone to critical issues and allocate testing resources accordingly. It helps testing teams focus on high-impact areas and detect issues early.
A use case in the e-commerce space: As e-commerce platforms grow in complexity and scale, they undergo frequent updates and enhancements to improve functionality, user experiences and performance. This results in unique challenges for traditional manual test case prioritization methods and tools. Machine Learning (ML) Tools, such as Q-learning, which is a model-free reinforcement learning algorithm to learn the value of an action in a particular state, can help analyze user behavior based in order to prioritize critical and frequently used features. This will ensure that testing of the most functionalities that are critical to a better user experience is prioritized and potential issues are identified and addressed promptly.

In conclusion, the integration of AI-driven tools for intelligent test case generation and prioritization has immense potential in enhancing test coverage. The continuous evolution of AI-driven solutions will undoubtedly play a vital role in the future of software testing, enabling early adopters to improve testing capabilities and mitigate risks, ensuring optimal performance and reliability of their software products.