Support Statement: Overcome the Limits of AI for Large Scale Software Modernization
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Q: Why should we use gmStudio instead of just using AI?
A: We believe the optimal solution is to use both. Allow us to elaborate…
There is no question that Artificial Intelligence platforms are bringing important and exciting changes to many fields: art, music, science, business, and computer programming. Technical users of AI are already routinely and productively using AI as a living technical reference for research and to expedite their development work. Even now (mid 2025), this WIKI is already summarized by chatGPT and the other AIs. The application of AI to the field of large-scale software transformation and modernization is particularly important to GM, and we are actively integrating AI into our platform.
Comparing Modernization Tooling: gmStudio + AI versus using AI alone
In our article comparing the tool-assisted rewrite to a more traditional manual-only approach, we describe how teams doing manual upgrades can benefit from also using gmStudio. Likewise, gmStudio can also benefit upgrade teams using AI. Note that gmStudio does not intend to compete with manual development, or AI-assisted development: on the contrary, gmStudio intends complement these activities allowing teams to deliver much more value with less risk and less effort. Some key points on this topic are presented below.
Point | gmStudio | manual-only |
|---|---|---|
Requires an AI properly trained on the detailed information from the legacy code and the required reengineering rules for optimal results | no | yes |
Teams may leverage gmStudio to prepare inputs to the AI and integrate outputs generated by the AI | yes | no |
Teams may leverage AI to assist with analysis, redesign, and verification | yes | yes |
Teams should have development skills in both the source and the target platforms | yes | yes |
Provides a flexible, deterministic, precise, and repeatable means of software transformation | yes | unlikely |
Typically require powerful hardware or SaaS | no | yes |
Can transform a collection of interrelated components as a coherent system | yes | unlikely |
May be driven by user-defined transformation rules | yes | unlikely |
Offers an extensible library of pre-built transformational rules | yes | unlikely |
Can process most large systems in less than 1 minute [1] | yes | unlikely |
Automates identifying and removing dead code | yes | unlikely |
Automates consolidating redundant code | yes | unlikely |
Automates customized COM API replacements | yes | unlikely |
Automates customized language transformations | yes | unlikely |
Automates integrating hand-written code | yes | unlikely |
Integrates easily with other DevOps tools | yes | unlikely |
[1] gmStudio will typically process up to 30K VB6 or ASP LOC per second.
Integrating gmStudio and AI
The role of AI in tool-assisted software modernization
AI has great potential to assist with the most difficult aspects of software modernization and other large-scale software maintenance projects:
Analysis to identify opportunities to improve legacy code
Analysis to identify and evaluate options for redesigning specific code
Analysis to facilitate developing and executing functional testing procedures
There is no question that AI holds great promise in the area of tool-assisted software reengineering, and teams all over the world researching this important topic. However, the problems are in the details. As I write this in Spring 2024, the application AI for large scale software reengineering still has a long way to go before it reaches its full potential. Here are some thoughts on how integrating gmStudio and AI may benefit large scale modernization work.
Preparing the Legacy System Model
Any significant legacy software system will contain at least 100K lines of code across dozens or hundreds of files. In fact, many of the enterprise systems we see are at least 5-10x this size and sometimes much larger. These system define and use tens of thousands of distinct symbols coming from multiple sources:
legacy and target computer languages keywords and file conventions
various internal services and data structures from the application domain, and
various external services and data structures from the legacy and target platform APIs.
These symbols describe the functionality of the legacy code in a detailed and deterministic way. We assume teams using AI will need to create an LLM and context with these symbols and then use AI to precisely read, interpret, and re-express the legacy functionality on the target platform. This is theoretically possible, but it takes significant expertise and meticulous processes to setup and complete effectively.
The AI model must be “taught” the symbols and the rules to be able to generate meaningful responses about those symbols. The various symbols will require metalanguage parameters that establish their semantics. There will be metadata and rules for the legacy platform, the target platform, and the for the accurate transformation of legacy code into target code. There will also be additional effort to assemble all of the AI generated output into a form that can be productively used built and maintained on the target platform.
In the case of VB6/ASP modernization, we suspect that the ambiguities and anachronisms of old VB6/ASP systems will impair the effectiveness the AI. gmStudio can be used to help prepare the LLM or to generate a .NET version of the legacy system. This information can then be input to the AI for help with analysing, redesigning, and refactoring the .NET code as a second step.
Generating Specialized Inputs and Outputs
Another critical input to AI-assisted software transformation is the user-defined specifications and coding standards for the system once it is rewritten for NET. There are many possible variations of how to write, and rewrite, software systems. The details of the desired coding standards must be accessible to the AI so that it can reliably and repeatedly produce results that follow standards and preserve functionality.
And there is also the task of generating supporting content: csproj files, resx files, config files, AssemblyInfo files, solution files, etc. These artifacts are not fully described by the legacy code; where will they come from? gmStudio is purpose-built to generate these types of files automatically according to user requirements and this will be helpful to teams using AI.
gmStudio.AI
gmStudio and the Tool-Assisted Rewrite methodology are purpose-built to provide an agile approach to system modernization that dramatically accelerates progress and reduces risk without sacrificing quality or control. The integration of gmStudio with AI provides additional opportunities for even faster delivery and improved technical and functional quality.
GM is using AI in our service delivery workflows where it makes sense. We intend to integrate AI more into our products and methods to flatten the learning curve for new users in 2026.
Please Contact Us to discuss how we can help you bring these two powerful technologies together for the most state of the art modernization project possible.
Well, you gotta love it. Below is the unedited response from my asking ChatGPT to evaluate the article above for accuracy.
The article presents a comparison between using gmStudio combined with AI and using AI alone for large-scale software modernization projects. Here’s an evaluation of the article for accuracy:
General Accuracy
Use of AI in Various Fields:
Claim: AI is bringing important changes to fields such as art, music, science, business, and computer programming.
Accuracy: True. AI is widely recognized for its transformative impact across various domains.
Tool-Assisted Rewrite vs. Manual Approach:
Claim: Teams using manual upgrades can benefit from gmStudio, and gmStudio can complement AI.
Accuracy: Plausible. Tools like gmStudio, which automate and assist in software transformation, can enhance the efficiency and accuracy of manual upgrades and AI-driven processes.
Key Points on gmStudio + AI vs. Manual + AI:
Requires a properly trained LLM:
gmStudio + AI: No.
Manual + AI: Yes.
Accuracy: Likely true. Automated tools like gmStudio reduce dependency on manually trained models.
Leverages gmStudio for input preparation and AI output integration:
gmStudio + AI: Yes.
Manual + AI: No.
Accuracy: True. Dedicated tools are designed to facilitate these processes, which manual approaches might lack.
Assists with analysis, redesign, and verification:
Both: Yes.
Accuracy: True. AI can assist in these areas regardless of whether gmStudio is used.
Requires development skills in both source and target platforms:
Both: Yes.
Accuracy: True. Expertise in both platforms is essential for successful transformation.
Provides a flexible, deterministic, precise, and repeatable transformation:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: Likely true. Automated tools are typically more reliable in maintaining consistency.
Typically requires powerful hardware or SaaS:
gmStudio + AI: No.
Manual + AI: Yes.
Accuracy: Plausible. Manual approaches augmented by AI often require significant computational resources.
Transforms a collection of interrelated components as a coherent system:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: Likely true. Tools like gmStudio are designed for system-wide consistency.
Driven by user-defined transformation rules:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: True. Automated tools allow customization through predefined rules.
Offers an extensible library of pre-built transformational rules:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: True. Libraries of rules are a common feature of such tools.
Processes most large systems quickly:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: True. Automated tools can significantly speed up the process.
Automates identifying and removing dead code:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: Likely true. Automated analysis can identify dead code more efficiently.
Automates consolidating redundant code:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: Likely true. Redundancy removal is another task well-suited to automation.
Automates customized COM API replacements:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: Likely true. Tools often have predefined mappings for such replacements.
Automates customized language transformations:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: Likely true. Automated tools are designed to handle language-specific transformations.
Automates integrating hand-written code:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: Likely true. Integration of custom code can be streamlined by automated tools.
Integrates easily with other DevOps tools:
gmStudio + AI: Yes.
Manual + AI: Unlikely.
Accuracy: True. Integration with DevOps is a feature of many automated tools.
Conclusion
The article accurately highlights the benefits of using gmStudio combined with AI compared to using AI alone for large-scale software modernization. It correctly points out that while AI can assist in various tasks, dedicated tools like gmStudio offer significant advantages in terms of automation, consistency, and integration. The detailed points comparing the two approaches are plausible and align with the capabilities of such tools.