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Future of testing in the world of ChatGPT and Brad

by mahesh.p

In recent years, the software field has seen significant advancement with the introduction of AI tools such as ChatGPT and Brad. Wherever you see, you would find a reference to these tools. Many more AI-based tools are available in the market. These tools have revolutionized the way software testing is performed and have brought about a noteworthy change in testing methodologies and processes. In this article, we will explore the future of testing along with ChatGPT and Brad and their impact on the testing world.

ChatGPT defines itself as a language model developed by OpenAI capable of generating human-like responses to the inputs given by the user. With its natural language processing capabilities, ChatGPT can be used for a variety of applications. On the other hand, Brad is an AI-based testing tool developed by Eggplant.

If we utilize the features of chatGPT and Brad it will make testing more accessible and more efficient. Let’s sneak a peek at how it is.

  • Bug Identification: ChatGPT can be used to identify bugs in applications by analyzing inputs in the form of natural language questions or commands. With the identified inputs generate a response using ChatGPT. You just have to give the inputs to ChatGPT and generate a response based on its AI abilities. Once the response is generated, analyze it for any errors. If the response is incorrect or incomplete we can report bugs.
  • Test Case Creation: Brad uses its AI abilities to automate the testing process by learning and adapting to the behavior of the software under test. Brad is capable of creating test cases automatically, reducing the need for manual effort in test case identification. It also identifies areas of the software that require improvement and suggests ways to optimize them.
  • Natural Language Processing: The ability of ChatGPT to understand and analyze natural language, can be utilized for testing applications that rely heavily on natural language processing.
  • Simulation: Testers can utilize ChatGPT to simulate real-world scenarios and test software for different use cases. It can identify issues that may not have been identified through traditional testing methods followed.
  • Continuous Testing: ChatGPT can be integrated into the CI/CD process and used for continuous testing. To integrate ChatGPT into the CI/CD process, a tester has to write automated tests that use ChatGPT to simulate conversations with the text-based components of the AUT. These AI tools can also be integrated with CI/CD tools such as Jenkins or Travis CI to automatically run tests.
  • Test data generation: chatGPT and Brad can be used to generate test data that covers multiple scenarios, including edge cases and outliers. Thus we can ensure the AUT is thoroughly tested with various inputs.
  • Test automation: ChatGPT can be trained to execute test cases automatically and provide results in a structured format.

The future of testing with AI tools is promising. With the increase in complexity of software applications, ChatGPT and Brad can help improve the accuracy and efficiency of the testing. As mentioned above these tools can help to reduce the time and effort required for testing, allowing for faster release cycles and improved time-to-market.

It is not that ChatGPT and Brad have only advantages in testing, it has numerous limitations also. Let’s check on those too.

  • Lack of human intuition: We had mentioned ChatGPT and Brad can identify the bugs and issues, but it lacks a very important factor – Human intuition and experience. We can’t be sure that it will identify all the issues in an application
  • Limited understanding of context: ChatGPT and Brad can be considered as good as the data they are trained on. They already claim that their data is limited after 2021, and this may cause an issue in understanding the context and variation of a particular application.
  • False alarm: Due to limitations of the dataset on which ChatGPT and Brad are trained it may raise false positives or false negatives. Again human intervention is needed to analyze these results.
  • The complexity of implementation: Though we had mentioned chatGPT can be integrated with CI/CD tools for continuous testing, this may require significant resources and expertise to set up and integrate.
  • Cost: To implement and maintaining AI-based tools for testing would require payment and would be too expensive. This will have a major impact on the budget for testing the application.
  • Lack of Lucidity: The results generated by ChatGPT and Brad can be difficult to understand. 
  • Data Security:  We cannot assure data security when we use chatGPT and Brad for generating test data for testing.

In summary, while ChatGPT and Brad offer numerous benefits for software testing, there are also potential drawbacks and limitations to think about. Considering the current limitations and drawbacks, we can be assured that the jobs of testers cannot be replaced by AI tools. A combination of both human and AI-based testing may be the most effective approach for ensuring the quality and reliability of software applications.

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