Differential GAI: Enhancing Software Engineering with Generative AI

Generative AI (GAI) is changing the way software engineers work by helping them write code faster and more efficiently. However, there are some challenges that come with using GAI, especially when it comes to the quality of the code it produces. The main issue is that GAI can create outputs that are not always reliable, which can cause problems for software projects.

#The Role of Verification and Validation

To make sure that the code produced by GAI works correctly, software engineers need to verify and validate the generated code. This process can be time-consuming and may reduce the productivity benefits that GAI offers. When engineers spend a lot of time checking the quality of GAI-generated code, it can take away from the efficiency gains that GAI was supposed to provide.

#A New Approach: Differential GAI

To address the challenges presented by GAI, a new approach called Differential GAI (D-GAI) has been proposed. This method takes advantage of GAI's ability to produce multiple versions of code and tests. Instead of relying on a single piece of code or test, D-GAI encourages comparing different versions to spot which one works best. By looking at various executions of similar code, engineers can gain a more reliable view of code quality.

#Introducing the Large-Scale Software Observatorium (LASSO)

To implement D-GAI, a platform called the Large-Scale Software Observatorium (LASSO) has been created. LASSO supports the process of D-GAI by allowing large amounts of code and tests to be executed and analyzed. This platform helps engineers systematically evaluate the quality of the code generated by GAI and can be used in various aspects of software development and research.

#The Challenge of Software Quality

Quality in software can be measured in many ways, but some of the most important aspects are related to how the software behaves. Unfortunately, many of these behavioral properties are difficult to measure automatically. In fact, it is often impossible to create a tool that can verify complex behavior for any code. This challenge means that a lot of effort in software projects goes into testing and checking, often accounting for over half of the total work required.

#The Dilemma of Quality in GAI Outputs

One potential solution to the verification and validation challenges is to use GAI to generate more tests. However, this creates a problem because those tests may also lack quality. It leads to a situation where engineers are trying to verify uncertain code with equally uncertain tests. The best way to handle this is to create many different versions of both code and tests, then compare them to see which performs better.

#Historical Context of Comparison Techniques

The idea of comparing different versions of software is not new. Techniques like Differential Testing, which compares outputs from different code versions, and N-version programming, which aims to make software more reliable by using multiple versions, have existed for some time. These techniques all share a focus on comparing executions of multiple tests to find the best-performing code.

#Emphasizing Comparative Testing with D-GAI

The authors of the D-GAI approach believe that its success in software engineering will depend on large-scale comparative testing of code generated by GAI. Existing testing frameworks, however, are usually designed only to test individual pieces of code and do not support the simultaneous examination of multiple code versions. Thus, current tools are limited in their capabilities for providing useful feedback on discrepancies.

#The Architecture of a D-GAI Engine

To create a D-GAI engine, it is essential to have the right components. LASSO can serve as the basis for developing this engine. It combines a testing platform with a repository of code to facilitate the N-version comparison that is central to D-GAI. By using LASSO’s features, engineers can manage the comparisons required for effective testing.

#How the D-GAI Process Works

When a request for code generation is made, LASSO can create a stimulus matrix filled with multiple implementations of the desired functionality and corresponding tests. This matrix includes different versions of the code, which can be obtained by running the generation process multiple times. By executing all available tests on the different code versions, engineers can gather results that reflect the performance of each version.

#The Benefits of N-Version Testing

N-version testing allows engineers to assess code from various perspectives. They can find differences in how the code behaves and use this information to enhance the overall quality. Even though testing many code versions might slow down response times, developers can work asynchronously with LASSO, ensuring that their workflows are not disrupted.

#Key Use Cases for D-GAI

D-GAI has various applications that can benefit software development. Some of the main use cases include:

1. Code Recommendation: By reviewing multiple versions of code, D-GAI can provide developers with more reliable recommendations for code modules. The recommended code is more trustworthy because it has been tested against multiple inputs.

2. Differential Testing: This technique can help identify issues in new code by comparing it with older versions. It helps reveal any faults that may have been introduced during development.

3. Test Quality Evaluation and Enhancement: D-GAI can help evaluate the effectiveness of tests by providing metrics that show how well they perform on different code versions. This evaluation can lead to improvement in the tests themselves.

4. Test Recommendation: Similar to code recommendation, D-GAI can suggest tests for specific code modules, ensuring that new tests are discovered and evaluated.

5. Test Oracle Recommendation: By comparing outputs from various code versions, D-GAI can help determine what the correct output should be for specific tests. This can reduce the need for manual evaluation of test results.

#Improving Code Model Evaluation

D-GAI also aids in evaluating the quality of GAI-generated artifacts. By comparing outputs from different coding problems, D-GAI helps ensure that the qualities of the generated code meet specific standards. The results from these evaluations can contribute to a better understanding of how effective GAI can be in software engineering.

#Conclusion

In summary, Differential GAI offers a new approach to enhancing the quality of generative AI outputs in software engineering. By examining multiple versions of code and tests, D-GAI provides a more comprehensive way to assess code quality. The introduction of the Large-Scale Software Observatorium (LASSO) supports this process, serving as a vital tool for developers and researchers. As more software projects adopt GAI, the techniques and platforms that support D-GAI will become increasingly important in ensuring that the generated code is reliable and trustworthy.