Following its successful launch on November 30, 2022, ChatGPT shattered industry benchmarks, amassing an impressive user base of over one million within its inaugural week. This generative AI technology has since showcased its robust potential across a broad array of technical and creative tasks. From developing meticulously curated articles, devising intricate machine learning algorithms, to automating data analysis workflows, it has made significant strides. 

The question thus arises: does artificial intelligence extend its capabilities to encompass software testing and test automation as well? For starters, it is essential to lay down a solid foundation of the concept of software testing before we delve into its potential intersection with artificial intelligence.  

What is Software Testing 

Software testing entails using manual procedures or automated tools to verify if software, a product, or its components meet the expected requirements and operate as intended. While software testing encompasses a diverse array of types, the two most prevalent ones are manual and automated testing. However, for the purpose of our discussion today, we will focus on automated software testing. 

Within the Software Development Life Cycle, test automation or automated testing implies conducting tests, controlling test data, and using the derived results to boost software quality, all while decreasing human involvement. This methodology substantially fortifies the quality assurance process. Nonetheless, it’s worth mentioning that a comprehensive and robust quality assurance procedure requires the concerted oversight of the production team, regardless of the robustness of automated testing. 

What is ChatGPT and How Will it Help With Test Automation 

ChatGPT, a product of OpenAI’s ingenuity, is an advanced conversational artificial intelligence model that utilizes the architecture of the Generative Pre-Trained Transformer (GPT). Rooted in the realm of natural language processing (NLP), it employs sophisticated deep learning techniques to decipher and comprehend the intricacies of natural language. 

To truly appreciate its potential role in software testing, it’s essential to delve into its applications, merits, constraints, and prospective influence within this domain. This exploration will provide a comprehensive understanding of how this cutting-edge AI model could revolutionize the landscape of software testing. So, without further ado, let’s dive deep into this fascinating topic. 

Benefits of Using ChatGPT for Software Testing Process and Test Automation 

Here are some benefits of using ChatGPT for software testing and test automation 

It has enabled testers and developers to create test cases and test data without manual intervention.  

ChatGPT leverages its natural language processing capabilities so that testers and developers can generate test cases and data using natural, easy-to-understand language commands. 

The test cases and data are tailored to your specific needs since ChatGPT understands the requirements of the software it’s testing. This has helped reduce the time and effort required to create cases manually and ensure they cover all necessary scenarios. 

Additionally, ChatGPT generates easy-to-understand test scripts to automate the testing process. The clear instructions and step-by-step guides from the artificial intelligence testing tutorial ensures that the testing process is streamlined and efficient and that the scripts remain accurate and effective. 

Low risk of error, less time and effort  

With ChatGPT, you can automate test cases using natural language commands to reduce the risk of errors compared to manual cases. This is because natural language commands are easier to understand and interpret than complex programming code and are less prone to errors. 

Also, ChatGPT automatically executes test cases based on natural language commands, which saves time and effort that would otherwise be required for manual execution. This automation process also ensures that test cases are executed consistently and accurately, with minimal errors or oversights. 

Since ChatGPT generates clear reports based on the results of the automated test cases, testers, and developers quickly identify any bugs in the software and take corrective action. 

Identify potential issues and defects earlier 

By analyzing results with natural language processing, ChatGPT detects patterns and trends that may reveal fundamental issues in an application. For example, if a specific feature consistently fails, ChatGPT analyzes the data and identifies potential causes of the problem, like a bug in the code or a problem with the foundational framework. 

ChatGPT’s machine learning capabilities can learn from past results and identify patterns that indicate potential issues or defects after a thorough result analysis. This allows ChatGPT to become increasingly accurate and effective at identifying potential problems over time. 

ChatGPT can be a valuable tool for software testing, particularly in natural language processing (NLP). Testers can interact with ChatGPT to evaluate its responses and identify areas for improvement. 

Did you know testers can use ChatGPT to generate automated test cases based on specific requirements? They also interact with ChatGPT to refine cases and ensure all possible scenarios are covered. Then, ChatGPT analyzes the results and generates reports that are easy to understand, highlighting any issues that require further attention. 

ChatGPT also identifies gaps in test coverage by analyzing the natural language used in requirements and identifying areas where you may need additional testing. This information will then be used to adjust the testing strategy to ensure all possible cases are covered. 

Moreover, this artificial intelligence (AI) in software testing can assist in data generation to ensure that the data is accurate and relevant. So, by interacting with ChatGPT and evaluating its responses, testers identify areas where the NLP model requires improvement. This feedback informs the model modification to improve its accuracy and effectiveness over time. 

Generate realistic test data for software products that require a vast amount of data for performance testing. 

ChatGPT’s natural language processing abilities benefit software products that require a large amount of data, including addresses, names, contact information, for performance testing. It employs natural language requirements to create test data that simulate user behaviors. This ensures that the performance testing covers all possible scenarios. 

The generated data mimics real-world scenarios and covers all possible cases, including those that might cause the system to malfunction or break down, which will be challenging to create manually. 

Also, the generated data may be customized to include specific data types, ranges, and formats to ensure it is accurate and relevant to the software product’s requirements. 

For example, if you want to format the test plan to input data and track bug like Jira or Github, you may leverage ChatGPT by prompting further in the same message thread for this scenario: 

Prompt: “Prepare a test case table to enter into Jira tickets.” 

ChatGPT: 

Train Chat GPT to create test cases 

Suppose a software product requires multiple test cases to cover different features, functionalities, inputs, outputs, and error handling. In that case, Chat GPT will create these relevant cases by understanding the natural language requirements. 

The generated test cases cover the edge cases and error scenarios that are hard to create manually, so the system is thoroughly tested, and every case is covered. 

The testers also interact with Chat GPT to analyze its responses and identify the areas that require improvement. This enhances the system’s accuracy and relevance in generating cases that meet all requirements over time. 

Possible Use Cases of ChatGPT in Automation Testing: 

Here are some ways ChatGPT can be employed in automation testing:  

Building automation test cases for different scenarios 

Chat GPT helps the testers create test cases and write scripts that include all ranges of scenarios and edge cases based on the user stories. It analyzes the natural language input to generate a human-like response so even non-technical stakeholders can participate in the testing process. 

You can now describe the scenario in English, and ChatGPT will automatically give corresponding test cases and scripts. For example, you can create cases for different requests, like ideas for testing a banking transaction. 

Test Data Generation 

ChatGPT excels in generating synthetic test data that encompasses various scenarios and edge cases. Leveraging its language understanding capabilities, it can simulate user inputs, API responses, or database records with diverse data combinations. This feature enables testers to create comprehensive test data sets for more thorough and effective testing. By automating the generation of test data, ChatGPT reduces the reliance on manual data creation, increases the efficiency of test execution, and ensures a broader coverage of test scenarios. Testers can benefit from the ability to quickly generate large volumes of realistic test data, which aids in identifying potential issues and uncovering hidden defects. 

Delivering easy-to-understand code and clear instructions on how to use the developed code. 

When developers write codes using natural language commands, ChatGPT employs its natural language processing capabilities to translate into actual programming code that is easy to understand. In addition to this, ChatGPT can offer insightful comments about potential problems with code snippets. It has been trained on a broad spectrum of coding problems and solutions, which enables it to identify potential issues and suggest improvements.  

Whether it’s a logic error, an inefficient approach, or a potential security risk, ChatGPT can provide valuable feedback to help users refine their code. It analyzes the code from multiple perspectives, mimicking the code review process conducted by experienced developers. This feature, coupled with its ability to generate documentation and write unit tests, makes ChatGPT an excellent coding assistant, helping developers write better, more reliable code. 

Also, ChatGPT provides real-time feedback to developers as they write code, allowing them to understand what they are doing and make any necessary corrections before the code is deployed.  

In addition to providing feedback to developers, ChatGPT assists in the software testing for AI based mobile software apps by generating test cases and scripts that are easy to understand and follow. 

Limitations of Using ChatGPT for Software Testing and Test Automation 

Unfortunately, there are limits to the functions of ChatGPT for software testing and test automation. Here are some limitations:

Potential bias and limitations in understanding certain contexts 

ChatGPT cannot recognize the context or purpose of software applications. As a result, incorrect responses may arise during software testing. 

 One of the biggest challenges of ChatGPT as one of the artificial intelligence testing tools is that it is heavily dependent on statistical patterns. This learning model employs statistical models to generate the next words based on the information consumed. However, there is no fundamental understanding of those words. 

This means that ChatGPT’s responses can’t be trusted when the user’s questions or statements require understanding a context that has not yet been explained. 

Limited Generation of Test Cases 

ChatGPT’s output may not always be comprehensive or relevant to build those edge tests and cases for the corner scenarios during software testing. 

The GPT-3.5 language model, established several years ago, is a deep-learning language model that was trained on a multitude of human-generated content datasets. Due to the time and data constraints during its training period, there may be scenarios it hasn’t encountered, leading to responses that could be inaccurate or outdated. 

On the other hand, ChatGPT-4, the latest iteration, brings a significant leap in efficiency and relevance. Leveraging more recent and diverse data, it provides a more accurate understanding of newer contexts and concepts. This advanced model has been fine-tuned to ensure more precise, up-to-date, and insightful responses, making it a powerful tool in today’s rapidly evolving digital landscape. 

Inability to Understand Code 

While it is true that ChatGPT, like many automated testing tools, doesn’t “understand” code in the human sense, it is capable of analyzing and interpreting the code within a given context. Coding is indeed an integral part of software testing, and while ChatGPT’s analysis might not replace a human developer’s comprehensive understanding, it provides valuable insights that help identify potential defects or bugs. 

When it comes to generating code, ChatGPT might produce incomplete snippets. That means, depending on ChatGPT alone for complete code development could pose challenges. As a developer, it is crucial to comprehend the generated code, customize it to meet specific requirements, and complete it where necessary. 

Despite these limitations, it’s important to note that ChatGPT provides a significant value-add in code analysis. It can scrutinize the code and recommend users on potential problems and issues. This ability to examine and provide constructive feedback makes it a valuable tool in the development process, contributing to the creation of more robust and reliable software. 

Lack of Execution power 

ChatGPT can provide particular test suggestions for execution. But since this artificial intelligence for software testing doesn’t understand code structures, it cannot execute the tests.  

Software testers still have to implement and evaluate the tests manually to spot these hidden factors that could cause an app to fail and subsequently find solutions. 

How ChatGPT Can be a Game Changer for Software Testing 

Here are some ways ChatGPT can transform software testing :

Automation of Repetitive Tasks 

It’s not automation in the traditional sense of controlling hardware or software directly, but rather, it’s about automating the intellectual work involved in software testing. 

Take data entry and verification, for instance. ChatGPT can be programmed to generate and verify vast amounts of test data based on given parameters, thereby significantly reducing the time and effort required for these tasks. Similarly, for test data generation, ChatGPT can quickly produce a variety of test cases based on the software requirements and scenarios provided to it. 

The automation here relates to ChatGPT taking over tasks which are repetitive in nature but require a degree of intellectual work – tasks that would otherwise be performed by human testers. The AI’s ability to handle these tasks not only improves efficiency but also allows human testers to direct their focus on more complex and creative aspects of testing. 

Reducing Human Error 

By automating these tasks, ChatGPT also plays a critical role in lowering the chances of human error. Manual handling of repetitive tasks can sometimes lead to oversights or inaccuracies, particularly when dealing with large volumes of data or complex test cases. The use of ChatGPT in these scenarios minimizes the risk of such errors, leading to more accurate and reliable testing outcomes. 

Boost test execution speed  

Unlike manual testing, where testers may take a considerable amount of time to execute test cases, artificial intelligence methods in software testing can manage multiple test cases simultaneously, saving time and cost and increasing efficiency. 

ChatGPT also analyzes test results in real-time, providing immediate feedback to testers and helping them to identify potential issues and defects as soon as they occur. This allows testers to take corrective action quickly and avoid significant problems later in the testing process, saving time and resources. 

With the help of ChatGPT, some tasks will be automated, thus accelerating the test execution process. It generates test cases automatically by analyzing the software requirements and creating a comprehensive suite of cases that cover all requirements. 

ChatGPT can generate test cases for various requests, including: 

• Sample data for a website login form 
• Testing ideas for an eCommerce transaction 
• Test data to reset password 

Prompt: “Generate some test cases for a feature that allows users to reset their password.” 

ChatGPT: 

Along with that, it will reduce the requirement of manual testing, which will encourage testers to give time to complicated tasks.  

With ChatGPT handling mundane testing tasks, testers devote their time to more complicated testing activities that require critical thinking and human intelligence. 

“The use of AI-based testing tools can help testers to focus on higher value tasks, such as analyzing results and providing recommendations, rather than spending time on repetitive and mundane tasks.” 

According to Diego Lo Giudice, Vice President and Principal Analyst at Forrester

Manual testing is time-consuming and tedious, and errors are inevitable due to human fallibility. But with ChatGPT, you can automatically generate cases by analyzing software requirements, ensuring that test cases cover all requirements. This significantly reduces the risk of human error and ensures that the most critical aspects of the software are thoroughly tested. 

ChatGPT can provide accuracy and consistency in smoothly running the test cases. 

Once test cases are integrated into the system, the testing process becomes more precise, reliable, and repeatable. 

This feature is particularly useful in identifying inconsistencies in the software’s behavior and addressing them promptly. Consequently, this helps to save time, as cases can be executed quickly and repeatedly. This speed and accuracy allow for the prompt detection of bugs and other issues, enabling developers to take corrective action early in the development cycle. 

End Note 

ChatGPT has many benefits that help development teams improve their software product quality. By automating repetitive tasks and reducing the risk of human error, artificial intelligence for software testing can provide faster and more accurate test results. 

However, its significant limitations suggest a need for an alternate approach for test automation like Symphony! Symphony increases the speed of software testing without compromising its best practices on quality assurance, accuracy, and efficiency approaches. Don’t hesitate to reach out to our QA consultants today for our expert advice on how to effectively use AI-powered QA automation tools in the delivery of your new product.