How Copilot can help testers in writing automated tests?
As software development accelerates, the demand for efficient and effective testing practices has remained unchanged. Automated testing is critical in ensuring that software is reliable and performs as expected. However, writing automated tests can be time-consuming and complex, especially as applications grow in scale and sophistication. This is where GitHub Copilot, an AI-powered code completion tool, can significantly impact.
Copilot is more than just a code-suggestion tool; it's a collaborative assistant that helps testers generate test steps, identify potential vulnerabilities, create Page Object Model (POM) components, and write robust assertions. It even aids in edge case identification, supports data-driven testing, and helps integrate tests seamlessly into CI/CD pipelines. This article explores how Copilot can empower testers to write more efficient, accurate, and reliable automated tests while streamlining the entire testing process.
1.Creating test steps for Test Scenario
Copilot will easily help you generate the steps for particular tests if you have written test cases or test scenarios.
Example of Copilot response:
In these cases, copilot assumes that you have a POM for the login page, home page, and any other necessary pages. If not - don’t worry, copilot will create it for you ;)?
2.Finding vulnerabilities in your code
Copilot will easily find vulnerabilities in your code, explain them, and offer a ready-made solution.
Example of Copilot response:
Sanitization: Added a sanitizeInput method to sanitize the status and type values before using them in regular expressions*.
(*please remember that we should limit the use of regex expressions)
3.Creating new POM components
Are you bored of creating each page for POM and clicking ctrl+c and ctrl+v? Just give it the locators and Copliot will create the whole POM file for you. You can give either the paths to the locators - Copilot will name them - or the names of the elements together with the paths.
Example of Copliot Response:
4.Writing Assertions
Copilot can suggest specific assertions based on the context of the test.
Example of Copilot response:
Copliot identifies all the necessary locators to create an assertion. And if we do not have them added to the POM, Copliot will add them for us.
5.Edge Case Identification
Copilot can suggest edge cases that might not be immediately apparent
Edge cases, which a Copilot found for the above scenario:
领英推荐
Copilot not only creates this scenario but also generates tests for each of them. Here is an example:
6.Data-Driven Testing
Copilot can suggest data-driven test cases, where tests are run with different datasets.
Data which was generated by Copilot:
So as we can see in the example above, copilot will not only generate test data for us but will also create tests based on this data.
7.Creating Test Documentation
Copilot can help write documentation or comments for test cases, improving readability and maintainability.
8.Integrating? with CI/CD
Copilot can assist in writing scripts that integrate automated tests into CI pipelines. Moreover, Copilot will explain to you the newly created file and the elements contained therein.
Remember that Copilot learns your project. Therefore, the more information it receives and the more you work with it, the more it can create better suggestions and hints and help you optimize your project.
Copilot can be a valuable tool for automating code writing, including generating test cases, but there are several disadvantages testers might face when using it for writing automated tests:
1. Limited Application Knowledge
Copilot generates code based on patterns and examples it has learned, but it doesn't have deep contextual knowledge of the specific application under test. This can lead to the generation of test cases that are syntactically correct but do not properly cover the real requirements, edge cases, or business logic of the application.
2. Repetitive or Generic Tests
Copilot often suggests generic or common patterns, which may not be optimized for the specific needs of a project. This can result in redundant, overly simplistic, or inadequate test coverage, as Copilot may not consider unique scenarios that require more tailored approaches.
3. Incorrect Assumptions
Automated suggestions from Copilot may lead to tests that pass or fail for the wrong reasons, resulting in false positives or false negatives. This can give testers a false sense of security, assuming code is correctly tested when in reality important aspects may be missed.
4. Security Concerns
The terms of service for Copilot do not specifically mention anything about the privacy of code generated by users. However, they do focus on protecting "Customer Data," which includes any data provided by the customer through the service. If you are concerned about privacy, you should read the Copilot terms and conditions, as the smallest license available may not provide as much privacy as you would like.
This is why in Exlabs we use Copilot for Business
Having Copilot for Business ensures code security and protects intellectual property. With Copilot for Business, companies can rest assured that their code is handled with the highest levels of security. Copilot for Business does not retain, store, or share code snippets, regardless of whether the data originates from public repositories, private repositories, non-GitHub repositories, or local files. This commitment to privacy helps businesses maintain control over their proprietary code and prevents any unauthorized access or sharing of sensitive data.
GitHub Copilot represents a significant advancement in the realm of software development, offering numerous benefits for testers writing automated tests. It can accelerate the coding process, reduce human error, and serve as a valuable learning tool. However, testers need to be aware of the potential downsides, such as over-reliance on AI, security concerns, and the lack of contextual understanding.
To maximize the benefits of Copilot while mitigating its risks, testers should use it as a supplementary tool rather than a replacement for their expertise. By maintaining a balance between AI-generated suggestions and critical thinking, testers can harness the power of Copilot to enhance their automated testing efforts while ensuring the quality and reliability of their code.
In the end, Copilot is a powerful ally in the tester's toolkit but should be handled with care, scrutiny, and awareness. When used thoughtfully, it can significantly boost productivity and learning, yet testers must remain vigilant in ensuring that the AI-generated code meets their specific testing needs and aligns with the overall testing strategy.
And If you don't already know how Copilot can help you with your project - just ask him ;)