Leverage Parasoft's ChatGPT to maximize artificial intelligence in software development

ChatGPT, one of the hottest topics of 2023, is a language model trained by OpenAI. It is able to understand and generate natural language text, and has been trained on large amounts of data, including the source code of many open source projects written in various programming languages.

Software developers can utilize a great deal of knowledge to assist in their work because it has a semantic understanding of the source code. Developers can use ChatGPT to generate useful code based on the right hints.

When writing code, ChatGPT is one of the applications in software development, and there are many other applications. The focus of this post is on ChatGPT generating code for professional software development environments.

There are already coding plugins available, such as Github Copilot, which uses the same technology as ChatGPT to help software developers. These plugins can analyze the code that developers are writing and generate recommendations for consideration.

With access to an extensive code base, ChatGPT can write accurate code and provide useful code suggestions, making the development process faster and more efficient.

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ChatGPT and code generation

The future of ChatGPT and AI code generation is bright, but there are some flaws. Let's take a look at the advantages and disadvantages.

Advantages of ChatGPT writing code

Although it is still in the early adoption stage, professional programmers are already realizing the advantages of taking advantage of ChatGPT.

• Supports multiple programming languages. Although it is difficult for ChatGPT to answer exactly how many languages it has been trained in, the most popular language used today is the one that is best represented in its training set.
• Increase productivity by generating code and provide more features such as unit testing, automated scripting, architectural planning, and recommendations for feature implementation.
• Deeper semantic understanding of source code than traditional artificial intelligence and machine learning (ML) tools. ChatGPT has shown that with the right hints, it can understand the functionality and behavior of your code. Its analysis examines the logic of the code and identifies potential edge cases, boundary conditions, and dependencies that may not be obvious on the surface. Traditional AI tools are limited to a smaller training set and limited output.
• Help developers who are not familiar with the language or target environment. ChatGPT helps developers new to the project quickly understand the environment and language they are using. Although there are some caveats, as described below, this is a good opportunity to learn this code.

Disadvantages of ChatGPT writing code

There are some drawbacks to writing code using ChatGPT because the technology is new and the source of the generated code is unknown. Here are some of the disadvantages of using ChatGPT in software development.

• IP disclosure occurs when using proprietary code and hints. The code used as a ChatGPT prompt becomes part of the training set and may be available to other users. Samsung employees recently discovered these risks when they leaked proprietary code to ChatGPT. Commercial solutions such as Copilot allow users to turn off the option to train with their proprietary code.
• The source of the generated code. Does ChatGPT's training data include proprietary or unlicensed open source code? Does the generated code need attribution or licensing? It's unclear where the code generated by ChatGPT comes from, as it's based on an example taken from the internet. Although the training data is mostly open source, you still need to understand the licensing requirements.
• Lack of consistency in responses to coding prompts. By design, each answer is likely and likely to be different every time the same prompt is used.
• Quality and security are of concern because errors in open-source data used in training and poorly written code can leak into ChatGPT's output, or generative AI models can make mistakes. ChatGPT does not review the security or performance of its answers, and the code it generates may not even compile.
• Developers can accept code with non-obvious problems in it. Since the code returns neatly formatted and documented, it's easy to take this for granted. It's more important than ever to apply good code quality processes to generated code, such as code reviews, static analysis, and unit tests, to ensure that it works as expected.
• ChatGPT is only as good as its training data. When standard development practices in the industry change, such as library APIs, some ChatGPT responses may contain outdated information.

Leverage Parasoft's technology to overcome ChatGPT's coding limitations

Parasoft is currently actively investigating synergies that can benefit customers by combining the advanced capabilities of generative AI models such as ChatGPT with the deep software test automation capabilities currently offered by Parasoft solutions. The study focuses on two main areas.

1. How to leverage ChatGPT in an automated software testing platform.
2. How to use the platform to verify ChatGPT-generated code.

Leverage ChatGPT in Parasoft's automated software testing platform

There is tremendous potential for synergy between ChatGPT and Parasoft's existing software test automation capabilities. We can leverage a deep semantic understanding of the code contained in GPT-based models to enhance the test offerings already included in the Parasoft platform, including test generation, static analysis, and reporting capabilities. Specifically, we focus on improving the following areas.

1. Generate code fixes for static analysis violations. Parasoft already has a wealth of information about coding standards and static analysis violations. We should be able to use that information to prompt ChatGPT to automatically generate fixes for found code violations.
2. Build intelligent, semantic-aware unit tests. Parasoft's current unit test generation capabilities are optimized to cover as many code paths as possible without creating redundant tests. We hope to be able to combine this with ChatGPT's semantic understanding of the code to produce tests with better names and a set of input values that achieve good code coverage and include real-world boundary condition values.
3. Generate positive, negative, and security API test scenarios. Parasoft's API test generator in Parasoft SOAtest does an excellent job of creating functional API test scenarios based on recorded traffic. But its ability to create scenarios for the traffic it sees. By leveraging ChatGPT, we hope to generate API test scenarios that run endpoints included in OpenAPI definitions in a variety of different ways, both expected and unexpected.

Use Parasoft's platform to validate ChatGPT-generated code

Parasoft can improve the usefulness of new GPT-based code generation by overcoming quality, security, and performance issues with generated code. Our platform is tested and validated in many different application areas, including safety-critical software. We already use artificial intelligence and machine learning to assist with static analysis, test generation, and self-healing.

While ChatGPT code generation is bound to become popular, it remains critical that the code it creates undergo vulnerability scanning, quality assessment, and testing, such as those provided by the Parasoft platform.

Benefits of ChatGPT and software test automation

Verify and validate the generated code. The team must analyze the code generated by ChatGPT with static analysis using software test automation tools and test it using test tools. The generated code should be treated like any other developed code, and even more carefully to ensure that it meets the quality and safety objectives of the project.

Adhere to coding standards. Solutions such as Parasoft Static Analysis ensure that the code generated by ChatGPT complies with coding standards and best practices. They can check the code and flag any violations against predefined rules and guidelines.

Improve security. The security of the code generated by ChatGPT is unknown, and vulnerabilities and related software flaws may be hidden in well-formed output. Treat all generated code like third-party or open source code. It should be reviewed before integrating into your application.

Increase productivity. AI code generation is likely to become an important innovation with the potential to dramatically increase productivity. For example, Parasoft's unit test generation technology is highly scalable for batch creation of test cases. In addition, improvements in the capabilities of software testing tools that leverage generative artificial intelligence bring additional benefits. A true win-win.

wraparound

Software organizations can improve test quality and minimize the manual work involved in designing and running tests by using software test automation solutions enhanced by generative AI technology. In the near future, Parasoft expects to release enhancements to its platform that use ChatGPT to help improve the accuracy and effectiveness of static analysis, unit testing, and API testing, which will ultimately better validate the results of ChatGPT-generated and human testing. The code written.

To fully benefit from this new AI technology, it is important to validate the code generated by ChatGPT and treat it as any newly developed code. Software test automation tools like Parasoft can help make ChatGPT a useful development tool while reducing risk and increasing productivity