We’ve reached a tipping point when it comes to AI. You’re either with it or against it! This polarizing new field is disrupting everything from how we work to what we do to why we do it. Many businesses are actively looking at how AI may assist them in achieving their digital transformation goals, but tangible applications can remain unclear. Let’s examine AI and ChatGTP in the context of software testing. What do you need to know? What’s impacting your bottom line, your testing practices, and your company’s goals?
Let’s dive in.
What is ChatGPT?
AI powered technology like ChatGPT is now a distinct reality that CIOs rank as their top strategic investments. But do you think integrating with ChatGPT’s language processor could improve software testing?
ChatGPT is a natural language processing tool that allows you to have human-like discussions with the chatbot, write prompts, ask questions, write code, and much more. Research startup OpenAI developed ChatGPT and launched it on November 30, 2022. ChatGPT is not directly involved in software development or automated testing. But it can generate test code for a variety of scenarios and inputs, assisting you in ensuring that your product is functioning properly.
According to the 2021–2022 World Quality Report, “The introduction of machine-based intelligence will be the most important solution to overcome increasing QA and testing challenges. After the introduction of risk-based test techniques and test automation technology, this will be the next major wave of change.”
Can Test Automation and ChatGPT Work Together?
Yes. According to Acceleration Economy, technologies like ChatGPT will revolutionize the way testing is executed. See specific functionalities of using ChatGPT for automation testing below.
- Test Case Development: ChatGPT can assist you in creating test cases and test scenarios for your software based on the parameters provided. For example, if a user scenario involves a user checking in to a website, ChatGPT can produce test cases that include scenarios such as an invalid username/password, an inaccurate captcha, an account being locked due to too many failed tries, etc.
- Test Data Generation: ChatGPT can generate the data required to test the software’s functionality. It can accept input from a specific test scenario or user story and then generate relevant test data. For example, if a user story involves a user purchasing a product, ChatGPT can create test data for various payment methods, products, payment and discount amounts, etc.
- Test documentation: ChatGPT can help you create and update test documentation, such as test plans, test cases, and test results, by generating descriptive content based on the information you provide.
- Exploratory Testing: ChatGPT can assist testers in exploring the software’s functionality and discovering any faults by exploiting its natural language processing skills.
- Bug Prediction: ChatGPT can also help predict potential flaws in software. It can highlight the hotspot areas that are more likely to contain issues by analyzing the code and testing data, allowing testers to focus their efforts on those areas.
- Communication between teams: ChatGPT can bridge the communication and collaboration gap between technical and non-technical team members by translating and converting natural language descriptions into test scripts.
It’s important to note that these are just the beginning of possibilities. This is in no way an exhaustive list or blog post. AI is a rising field with many untapped and untold possibilities.
A real-life example that shows how Octopus Energy has received higher customer satisfaction with ChatGPT
Octopus Energy, a UK-based energy company, has integrated ChatGPT into its customer service channels and claims that it now handles 44 percent of all consumer questions. According to CEO Greg Jackson, the program currently performs the work of 250 employees and achieves greater customer satisfaction scores than human customer support workers.
Cons Of Using ChatGPT For Automated Testing
ChatGPT has shown a lot of potential in the field of automated testing in a relatively short period of time. Regardless of the benefits of AI in software testing, using ChatGPT test automation has certain drawbacks, which include the following:
- Accuracy constraints: As an AI model, ChatGPT may provide inaccurate or incomplete test cases that may not fully cover the application’s functionality. To assure the quality of test cases and scripts, human validation is still required.
- Integration issues: Integrating ChatGPT into a current test automation workflow can be difficult, requiring knowledge of API integration as well as changes to meet individual needs.
- Intellectual property issues: Using AI-generated code may raise intellectual property concerns because the resulting test scripts may look similar to existing copyrighted code.
- Manual dependency: Just because you can create code snippets does not mean that you have a fully functional application. There are still many steps between the code and the final product. It still requires a significant amount of manual effort for the final product’s delivery.
Will ChatGPT Take the Place of Human Testers?
ChatGPT is usually the best solution for automating repetitive activities, time-consuming tests, tests that cannot be performed manually, and tests with multiple data sets. However, we will still need human testers since humans understand what other humans require, and machines are still a long way from obtaining “common sense.”
We will still require creative, highly trained QA engineers to use their insights throughout the product development lifecycle.
To summarize, ChatGPT can aid in test automation efforts but is not yet sophisticated enough to replace human testers.
What Opkey Offers
No-code testing tools like Opkey make space for less technical users to get more involved with the software delivery lifecycle. Opkey’s no-code test builder can instantly generate automated test cases, enabling business users and IT to automate and scale testing efforts. Opkey also simplifies end-to-end testing efforts across all layers of modern enterprise architectures. With its features like test mining, change impact analysis, and Self-healing reusable and resilient tests can be created significantly faster and maintained effortlessly.
- Process Automation: Opkey’s Test Mining feature can automate time-consuming testing operations. This can free up valuable time and resources that can be put to better use.
- Better Decision Making: Opkey’s intuitive dashboard and reporting system can assist organizations in understanding trends in project performance, identifying areas for development, and making resource allocation decisions.
- Improved Customer Service: AI-powered testing solutions like Opkey can assist organizations in responding to consumer questions and resolving issues more rapidly. This can lead to enhanced client satisfaction and loyalty.
- Improved Compliance: Opkey adheres to a variety of norms and standards for ERP implementation and testing. Thus, it can automate compliance checks, such as detecting conflicts of interest and verifying that applications adhere to legislation and standards.
According to Forrester, 55% of organizations have not yet generated any tangible business outcomes from AI, and 43% believe it is too early to tell.
AI-enabled testing will become a major component of QA as organizations strive for quicker software development. AI-based tools like ChatGPT will make it easier to detect errors faster and help decide what needs to be tested and where bugs are likely to be found. Due to the fact that the algorithms’ underlying software will make decisions based on data provided by humans, this will increase the demand for skilled, specialized QA specialists in the future.
It would be wiser to simply accept AI’s growth as a fact and then devote that energy to finding productive, good ways to integrate it into our lives. As technology advances, we expect ChatGPT to become even more sophisticated and capable. However, like with any AI technology, it is critical to use ChatGPT ethically and responsibly, ensuring that it has been trained on diverse and unbiased data sets.