Gen AI is a Game Changer for Coding (Part 2)

Modernization projects are evolving legacy systems to meet the demands of today’s technologies while staying nimble enough to innovate for the future. Developers face a complex reality of technical debt, code inefficiencies, and the growing expectation to deliver faster without compromising quality. For many organizations, the process feels like navigating a maze of mounting deadlines and escalating costs.

When we speak with our clients its very clear that Generative AI (Gen AI) has quickly become a game changer that is redefining how we approach modernization. It’s transforming the developer’s entire workflow and freeing them from cumbersome tasks to focus on creativity. It’s amplifying their craftsmanship skills. 

For organizations, Gen AI offers a smarter, faster path to modernization, and enables teams to scale efforts with precision and collaboration. Future-ready app portfolio enterprises are using Gen AI to empower developers to write better code faster—which accelerates application modernization efforts.

The stage is set for a new era of smarter development.

Gen AI supercharges innovation-ready organizations

Gen AI success is realized when organizations approach its adoption with a strategic lens and integrate it into a broader maturity journey: 

• Standardization: Documenting and defining best practices.
• Industrialization: Implementing tooling to improve consistency.
• Automation: Using advanced tools to handle repetitive tasks.
• Gen AI: Elevating capabilities through context-aware automation that enables code generation, transformation, and remediation at scale.

Reaching this level of maturity unlocks the true potential of Gen AI code generation, which enables app modernization to become a competitive advantage, rather than a bottleneck. Gen AI helps automate time-intensive tasks, bridge skill gaps, and simplify legacy system complexities, which all lead to the empowerment of development teams to innovate and scale with greater efficiency. The result? Consistent, high-quality outcomes that keep pace with the demands of an ever-evolving digital landscape.

Today’s Coding Challenges

We code to create, to fix, and to evolve. Coding is how we keep applications relevant and reliable. It’s the means through which we innovate with new features and address issues in the tools and platforms we use.

In part 1 we looked at the first of three primary purposes of coding in the context of app modernization, and the unique challenges that development teams encounter in each scenario:

1. Developing a new feature to meet business needs or create competitive advantages.
2. Remediating issues—bugs, vulnerabilities, or performance bottlenecks that disrupt operations.
3. Modernizing legacy systems to leverage shifts in technology or reduce technical debt.

For part 2 we’ll explore how Gen AI addresses the second primary purpose of coding: Remediating issues such as bugs, vulnerabilities, and performance bottlenecks that disrupt operations.

Scenario 2: Remediating something that went wrong

When issues arise—whether reported by clients or identified internally—developers face the dual challenge of fixing the code and navigating the complexities of the remediation process.

Consider a scenario where a client reports an issue with an application’s functionality, or the technical team identifies a critical bug affecting performance. While the developer might use tools like GitHub Copilot to propose a solution or fix, the challenge extends far beyond the code itself and always involves a series of time-consuming tasks:

• Qualifying the issue: Understanding the scope and impact of the problem.
• Locating files: Identifying the specific files and code segments that need modification.
• Validation planning: Determining who will verify the fix and how.
• Setting up test environments: Creating isolated environments to implement and test the solution without affecting production systems.

Even when the solution is straightforward, the complexity of the remediation process—issue qualification, validation, testing, and so forth—makes implementation anything but simple.

Why Gen AI is a game changer

Gen AI tools like GitHub Copilot are revolutionizing the remediation process by simplifying and streamlining each stage. They provide an ecosystem around the code, enabling issue creation, qualification, and remediation within the same environment. 

Developers can use AI to identify affected files, propose fixes, and track changes. The tools show comparisons of the code before and after modifications. They create Pull Requests for the changes with proper change comments.

Changes can be tested in controlled, non-production environments keeping the integrity of production systems intact. And regression tests and automated validations ensure that changes align with previous development efforts, capitalizing on what’s already in place. 

As remediation processes evolve, organizations benefit by standardizing and integrating Gen AI tools into their workflows. The right tooling will help identify and resolve issues faster, ensure consistency in how fixes are implemented and validated across teams, and reduce dependencies on manual processes that can delay fixes or introduce variability. It goes a long way in bringing about the clarity and accountability needed for efficient remediation.

Our recommendations

To streamline and scale the remediation process, consider the following best practices for integrating Gen AI tools and workflows.

Leverage end-to-end platforms.

Use platforms that integrate the entire remediation lifecycle—from issue creation to validation. Tools like GitHub Copilot not only propose solutions but also streamline workflows, allowing developers to focus on the fix rather than peripheral tasks.

Capitalize on existing processes.

Build on existing practices such as automated testing and regression tests. Integrate Gen AI into these established workflows to reduce friction and ensure compatibility between new fixes and previous code. 

Sogeti’s Gen AI Amplifier for Software & Quality Engineering, for example, contains pre-crafted prompts to speed up critical steps across the end-to-end quality engineering process within the software development lifecycle, which leads to improved quality outcomes and efficiency from the get-go.

Balance automation with oversight.

While Gen AI tools automate much of the remediation process, human validation remains essential. Developers should retain responsibility for reviewing AI-generated solutions, testing for edge cases, and refining fixes to align with organizational standards.

Remediating issues is rarely as simple as it seems, but Gen AI simplifies the journey from problem identification to resolution. Organizations should integrate AI-driven tools into the remediation process to address challenges faster, more consistently, and with reduced overhead—which means developers deliver more fixes without compromising quality.

In our next and final part, we’ll explore why modernizing legacy code due to tech shifts and technical debt is urgent. Cloud migration using PaaS faces compatibility challenges and security risks. And how Gen AI simplifies updates, making modernization strategic and efficient.

Missed Part 1 of our 3-Part Series? Catch up on how Gen AI is revolutionizing development – automating tasks, bridging skill gaps, and accelerating innovation to keep businesses ahead. Read Part 1, here.

/Pierre Olivier Patin & Mahesh Jadhav