For some, artificial intelligence is only about developing new applications, but Gen AI can be very effective in legacy modernization. The tool is crucial to plan modernization, develop new solutions to replace old ones, and even migrate information between environments. 

Check out below the recommendations for adopting Gen AI in legacy modernization, ensuring efficient results, more productivity, better quality and assertive code, as well as automation of developer tasks.

Introduction to Gen AI in Legacy Modernization

Gartner expects that by 2027, generative AI tools will be used to create suitable replacements for legacy, reducing modernization costs by 70%.

The challenges of modernizing laggards goes beyond the high costs of this process. Generative AI in legacy modernization helps overcome several obstacles, including creating new solutions to replace obsolete technologies, supporting migration to cloud environments, transitioning from monolithic architecture, and adopting microservices.

In addition, contextualized generative artificial intelligence becomes an ally for developers, as shown by these examples:

• Speeding up their onboarding;
• Reducing technical debt;
• Automating routine development and modernization of applications;
• Reducing cognitive load;
• Assertive code suggestions, and much more.

StackSpot AI: context and efficiency

StackSpot AI is an artificial intelligence code assistant with a big advantage: it can use context in software development. 

You can access Knowledge Sources within the platform, which works as an internal library. After Knowledge Sources are enriched with standards used in your company, AI will suggest more assertive and higher-quality code than generic AI tools.

Watch the video below about StackSpot AI in legacy modernization.

Best practices for adopting Gen AI in legacy modernization

Thinking about the benefits that modernization brings to software development and the potential of artificial intelligence, we’ve put together six practices for success:

1 – Clear communication 

Modernization is a complex and challenging process, so senior professionals and tech leads play an essential role in fostering a culture of collaboration and clear communication. 

Open communication will be essential for all sides to justify their points of view during the modernization process. Feedback is also necessary to correct problems quickly without disturbing the ongoing process.

Clear communication channels must be established, and the people responsible for each process must be designated. This approach will reduce side conversations and noise and encourage collaboration between people and teams.

Many doubts and even insecurities come into play during legacy modernization. After all, this process is not only technical but also cultural. Avoid unproductive issues and ensure that teams work towards the same goal.

The same goes for the adoption of Gen AI in legacy modernization. Present it as an ally that will improve the Developer Experience in a time-consuming and complex process. 

2 – System assessment 

The initial step in the legacy modernization process is the system assessment. This phase includes decisions involving task prioritization, systems optimization, identification of systems for modernization, and other considerations.

During this assessment, professionals should pay special attention to four aspects:

• System perspective: includes system architecture; the scenario to which the system belongs; team size, skills, and functions; and development methodology.
• Processes: demand, change, quality, and project management.
• Stacks and tools: problem tracking, source code, and knowledge base management.
• Development lifecycle: analysis of all stages —from conception, analysis, architecture, and design to implementation, including code quality and security stages. 

A contextualized AI tool can make the evaluation of a legacy system more efficient. Feeding AI with the company’s standards and technologies ensures assertive suggestions for the project context, and legacy applications can even be converted into modernized solutions.

AI will also produce gains via automation, for example, in refactoring and code quality processes such as testing, which minimize errors and enhance the efficiency of the legacy modernization process.

3 – Take advantage of automation and DevOps practices

Hyper-contextualization allows a Large Language Model (LLM) to understand your code, style, stack, structures, and more. With all the information you provide, an AI assistant directly connected to your workspace can better understand how your team codes and will provide more targeted solutions.

With this in mind, at StackSpot AI, we have Quick Commands —automated and pre-defined actions that allow information to be exchanged quickly with the LLM. The tool can improve tasks requiring significant effort, increasing productivity and workflow efficiency.

For example, engineering professionals can streamline the modernization process and ensure faster and more reliable implementations by adopting automated tests, continuous integration, and implementation pipelines.

4 – Balancing AI and human collaboration

Even with the potential of Gen AI in legacy modernization, human oversight brings valuable insights and a contextual understanding that complements AI’s capabilities. 

When dealing with high complexity or major ethical and legal requirements, human intervention is essential to ensure reliable and responsible results.

Today, professionals are responsible for training and improving generative AI models. This task involves expertise in machine learning, big data, and a deeper understanding of the underlying algorithms. It requires specialized skills to ensure that AI models are accurate, efficient, and ethical. 

The balance between AI and human supervision implies a collaborative approach, allowing humans to deal with more complex challenges and develop innovative solutions. Meanwhile, AIs mitigate risks and ensure compliance with human needs and values.

5 – Continuous monitoring

Gen AI in legacy modernization will support ongoing monitoring of production systems, identifying performance and security anomalies. It can even suggest updates and corrections automatically, contributing to more robust and simplified software maintenance.

Finally, AI plays a vital role in error detection and static code analysis. Analysis tools based on artificial intelligence can:

• Identify suspicious and problematic code patterns;
• Assist in the early detection of security vulnerabilities;
• Recommend coding practices, among other actions.

Importantly, the performance of AI tools also needs to be monitored. This action is key to certifying the accuracy of AI suggestions and keeping modernization on track.

6 – Choosing the right AI tool 

Gen AI in legacy modernization is incredibly helpful, but a contextualized tool is a step ahead of other companies. Artificial intelligence based on your context, such as StackSpot AI, will provide more objective and suitable solutions.

Other important requirements should be considered when choosing the best AI tool for your business:

• Integration with other tools you already use;
• Capacity for continuous learning from the information you provide;
• Monitor the speed and performance of this AI so that your development cycle is not negatively impacted;
• Security and privacy for the information fed to the tool.

Want more details? We have an infographic about 5 factors that influence your decision to choose an AI code assistant. Check it out on LinkedIn.

StackSpot AI in practice

Speaking of a contextualized tool, StackSpot AI can use your context, knowledge sources, and technological decisions about standards to create code that has better quality and is more assertive.

The AI code assistant also promotes increased productivity, easy contextual input, and code compliance.

An excellent example is the modernization of legacy mobile using StackSpot and Gen AI. The migration to Compose resulted in more agility and productivity. Find out more in the article on our blog.

Conclusion

Generative artificial intelligence can be incredibly helpful in a successful legacy modernization strategy, especially when good practices are adopted, such as using the company’s context to your advantage.

From clear communication to making the most of automation, AI will contribute to the modernization process, accelerating, simplifying, and promoting a better experience for developers.

After learning about the benefits and best practices in the adoption of Gen AI in legacy modernization, you can move forward with your system upgrade. To have StackSpot with you on this journey, contact our team of experts.

References

• Gartner’s Top Strategic Predictions for 2024 and Beyond | Gartner
• IA generativa irá mudar drasticamente o desenvolvimento de softwares e soluções | Tecmundo
• Integrating Artificial Intelligence into the Modernization of Legacy Systems | Ivan B. Linde
• O Uso das Inteligências Artificiais na Área de Desenvolvimento de Software | DIO
• Unleashing developer productivity with generative AI | McKinsey