Benefits of Code Modernization

Upgrading to the latest Java version: It's not just about performance. Remember when we used to type cast objects from collections and hope it wouldn't throw a ClassCastException? Or write dozens of lines for simple data classes with getters, equals, and hashCode? Java's evolution has been consistently focused on making developers' lives easier by reducing cognitive load. Each release brings features that align better with how our brains naturally process information. From Java 8's streams transforming nested loops into declarative pipelines, to Java 14's records eliminating boilerplate, to Java 16's helpful NullPointerExceptions telling us exactly what was null - these aren't just "nice to have" features. They're investments in sustainable development practices that reduce mental overhead. The science is clear: our working memory can only handle 3-5 chunks of information at once. Modern Java features let us focus more on solving business problems and less on syntax and boilerplate. So when making the case for upgrading, don't just talk about performance and security. Talk about developer wellbeing, code maintainability, and the cognitive benefits that come with modern language features. Your brain - and your future self who has to maintain that code - will thank you. #Java #SoftwareDevelopment #DeveloperProductivity #MaintainableCode

Continuing the LinkedIn takeover this week, today I’m turning to a topic that doesn’t always make headlines, but is rapidly becoming one of the most powerful GenAI applications in IT: legacy modernization. At BCG, we see many CIOs racing to deploy AI copilots, automate workflows, and build new digital products. But too often, they’re layering these innovations on top of brittle, outdated tech stacks. With the emergence of agentic AI (systems that can reason, plan, and adapt autonomously) organizations can now take on modernization efforts that were once seen as too slow, too costly, or too risky. However, despite its potential, legacy modernization remains the least adopted GenAI use case in IT. Our data shows that only 2% of companies have fully deployed it, even though it offers up to 40% productivity gains, 35% cost savings, and one of the strongest long-term payoffs. Case in point: A leading financial institution in Asia faced a sprawling complex legacy codebase - decades old, poorly documented, deeply embedded in critical systems. With GenAI, they conducted a POC for recreating legacy application using modern software development tools going through their existing SDLC process: - Parsed and analyzed hundreds of lines of code - Automatically generated dependency maps - Surfaced refactoring recommendations - Create unit and regression test cases - Translated modules into modern languages The result? 🔓 Clearer modularization strategy 📜 Faster re-platforming to cloud-native ✅ Fewer regressions – GenAI flagged change risks early, reducing breakage and costly rewrites What once took years now takes months. Legacy modernization may not feel glamorous, but it’s a critical enabler for scale, agility, and GenAI readiness. This share concludes my week on Vlad’s account, and it’s been a great experience. I hope those who have enjoyed my posts will follow my own LinkedIn ahead of Ruth Ebeling’s takeover next week. I’ll leave it to her to introduce her chosen topics. #LegacyModernization #EnterpriseIT Sumit Kumar, Kunaal Wadhwa, Syed Husain > Dr. Michael Grebe - Takeover during Vlad's summer sabbatical <

Over the weekend, I read Google's paper on how they use AI for internal code migrations—and it’s packed with insights on how to approach legacy system modernization. I’ve attached the paper for those interested, but here’s how I believe some of these strategies can help us tackle complex modernization challenges: 🔎 1. Accelerating Legacy System Modernization Google leverages Large Language Models (LLMs) to automate large-scale code migrations, significantly reducing manual effort and speeding up projects. Applying similar AI-driven approaches can streamline the modernization of legacy systems, cutting through complexity and outdated code. 🔎 2. Combining AI with Proven Engineering Tools By blending LLMs with Abstract Syntax Tree (AST)-based tools, the ensure accuracy and scalability in their code transformations. This hybrid method shows how AI and traditional engineering techniques can work together to deliver safe and reliable modernization. 🔎 3. Reusable Migration Workflows Google created modular, reusable workflows that make onboarding and executing new migration tasks faster and more efficient. Developing similar toolkits for legacy systems could simplify recurring modernization steps and adapt to complex scenarios. 🔎 4. Measuring Success by Business Impact Google focuses on measurable outcomes, like a 50% reduction in project time, rather than just the volume of AI-generated code. This business-aligned metric highlights the importance of demonstrating clear ROI in technology transformation projects. 🔎 5. Safe and Scalable Rollouts Their phased deployment strategy ensures AI-driven changes are rolled out safely, minimizing disruption. Adopting a controlled rollout approach can help manage risks and ensure stability when modernizing critical systems. 🔎 6. Strategic Use of AI Models Google balances using custom fine-tuned models and general-purpose tools depending on the task. This approach offers valuable insight into when to invest in specialized AI solutions versus using adaptable off-the-shelf models. 📌 The Big Picture: Legacy system modernization is about combining AI-driven efficiency with engineering best practices to deliver faster, safer, and more impactful business transformations. 📎 I’ve attached the paper if you’d like to explore it further! #LegacyModernization #GenAI #BusinessInnovation — Enjoyed this post? Like 👍, comment 💭, or repost ♻️ to share with others.

𝗔 𝗹𝗼𝘁 𝗼𝗳 𝘄𝗵𝗮𝘁 𝘄𝗲 𝗱𝗼 𝗶𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗰𝗮𝗻 𝗯𝗲 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀. In modern programming, no one would hard code values everywhere in an application. They would create variables, functions, and structures that make it easy to adapt when requirements change. In manufacturing controls, buffering inputs and outputs plays the same role. It is the bridge between the raw hardware signals and the logic that drives your process. 𝗪𝗵𝗲𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 𝗰𝗿𝗲𝗮𝘁𝗲 𝗯𝘂𝗳𝗳𝗲𝗿 𝗹𝗮𝘆𝗲𝗿𝘀, 𝘁𝗵𝗲𝘆 𝗮𝗱𝗼𝗽𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝘁𝗵𝗮𝘁 𝗮𝗹𝗶𝗴𝗻 𝘄𝗶𝘁𝗵 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀. Instead of sprinkling raw I O points across every routine, they centralize, name, and document them. This makes the code easier to understand, easier to troubleshoot, and easier to scale across multiple machines or facilities. The benefits go beyond troubleshooting: ● Reusability of logic, since common structures can be applied across projects ● Maintainability, as changes to hardware assignments only require one edit ● Standardization, ensuring that multiple engineers follow the same conventions ● Training efficiency, because new engineers can learn faster with clean and clear code 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻. For managers and decision makers, adopting software development best practices in control logic directly affects performance on the plant floor. A single program written with clarity can save hours of downtime during a failure. Scaled across an enterprise, that clarity reduces startup time, improves consistency, and lowers the cost of training. The lesson is simple. When we treat PLC programming as true software development, not just wiring logic into code, we build systems that are more resilient, more scalable, and ultimately more valuable to the business.

September 2023: Sole data engineer leaves. Operations paused. Major crisis. January 2024: Automated pipelines. self-serve reports. 25+ hours p/w saved. Here's how we achieved this for a fast-scaling telehealth firm: When their only data engineer handed in his notice, every department - from operations to sales, finance to the C-suite - suddenly found themselves scrambling. Their entire reporting stack relied on brittle SQL scripts and manually generated CSVs. Without that one engineer to babysit the workflows, key processes ground to a halt. This wasn’t just an inconvenience; it became an immediate operational and HIPAA compliance risk. So, what did we do? 𝟏. 𝐑𝐞𝐟𝐚𝐜𝐭𝐨𝐫𝐞𝐝 𝐜𝐨𝐫𝐞 𝐒𝐐𝐋 → Rewrote over 30 core queries. → Slashed execution times by 60% → Set the foundation for scalable, repeatable workflows. 𝟐. 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 → Built a suite of Python scripts that automatically handle validation, transformation, and reformatting. → Brought manual errors down to 0 → Delivered a reusable codebase for future use cases 𝟑. 𝐒𝐞𝐜𝐮𝐫𝐞𝐝 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 → Implemented a scheduled, audited email automation system → Sends appropriate files to the right people → Saved 8–10 admin hours per week → Created a full audit trail for compliance 𝟒. 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐬𝐚𝐥𝐞𝐬 → Built one-click EMR exports that gave them instant access to the data they needed. → Prep time dropped by 90%, → Made client follow-ups seamless 𝟓. 𝐓𝐫𝐚𝐢𝐧𝐞𝐝 𝐭𝐡𝐞 𝐧𝐞𝐱𝐭 𝐡𝐢𝐫𝐞 → Documented every pipeline, SQL convention, and Python script → Spent several weeks training the incoming engineer 𝐓𝐡𝐢𝐬 𝐫𝐞𝐬𝐮𝐥𝐭𝐞𝐝 𝐢𝐧: 25+ hours p/w saved across teams through automation Tightened HIPAA compliance posture Real-time insights for decision-makers A sustainable system that outlives any one person 𝐓𝐋;𝐃𝐑: If your healthcare org still runs on patched-together scripts and one data engineer, you’re one departure away from disaster. Modernize with automation Secure distribution Do intentional training ... and watch your risk, costs, and bottlenecks vanish. ♻️ Share this to help someone in your network Follow me for more on data modernization in healthcare.

Exactly a year ago, we embarked on a transformative journey in application modernization, specifically harnessing generative AI to overhaul one of our client’s legacy systems. This initiative was challenging yet crucial for staying competitive: - Migrating outdated codebases - Mitigating high manual coding costs - Integrating legacy systems with cutting-edge platforms - Aligning technological upgrades with strategic business objectives Reflecting on this journey, here are the key lessons and outcomes we achieved through Gen AI in application modernization: [1] Assess Application Portfolio. We started by analyzing which applications were both outdated and critical, identifying those with the highest ROI for modernization.  This targeted approach helped prioritize efforts effectively. [2] Prioritize Practical Use Cases for Generative AI. For instance, automating code conversion from COBOL to Java reduced the overall manual coding time by 60%, significantly decreasing costs and increasing efficiency. [3] Pilot Gen AI Projects. We piloted a well-defined module, leading to a 30% reduction in time-to-market for new features, translating into faster responses to market demands and improved customer satisfaction. [4] Communicate Success and Scale Gradually. Post-pilot, we tracked key metrics such as code review time, deployment bugs, and overall time saved, demonstrating substantial business impacts to stakeholders and securing buy-in for wider implementation. [5] Embrace Change Management. We treated AI integration as a critical change in the operational model, aligning processes and stakeholder expectations with new technological capabilities. [6] Utilize Automation to Drive Innovation. Leveraging AI for routine coding tasks not only freed up developer time for strategic projects but also improved code quality by over 40%, reducing bugs and vulnerabilities significantly. [7] Opt for Managed Services When Appropriate. Managed services for routine maintenance allowed us to reallocate resources towards innovative projects, further driving our strategic objectives. Bonus Point: Establish a Center of Excellence (CoE). We have established CoE within our organization. It spearheaded AI implementations and established governance models, setting a benchmark for best practices that accelerated our learning curve and minimized pitfalls. You could modernize your legacy app by following similar steps! #modernization #appmodernization #legacysystem #genai #simform — PS. Visit my profile, Hiren Dhaduk, & subscribe to my weekly newsletter: - Get product engineering insights. - Catch up on the latest software trends. - Discover successful development strategies.

Legacy PLC code can finally get the documentation it deserves — thanks to MCP + AI. Most factories are running PLC projects that have been patched, extended, and “quick-fixed” for years — often with minimal comments and unclear logic. With the MCP Server CODESYS, an AI assistant can load the entire project, scan every POU and variable, and instantly highlight issues: magic numbers, duplicated logic, inconsistent naming, missing comments. Even better — it can auto-generate a Markdown report describing each POU, summarize logic flows, suggest better variable names, and insert comments where context is missing. For maintenance and modernization work, this is huge: instead of spending days trying to “decode” legacy logic, engineers start with clarity, structure, and a guided refactoring path. This is what AI-supported engineering actually looks like in practice — not replacing engineers, but giving us back the time we lose understanding old code.

𝗙𝗿𝗼𝗺 𝗖𝗵𝗼𝗿𝗲 𝘁𝗼 𝗖𝗵𝗮𝗺𝗽𝗶𝗼𝗻: 𝗛𝗼𝘄 𝗔𝗜 𝗶𝘀 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗠𝗮𝗸𝗶𝗻𝗴 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝗶𝗻𝗴 𝗮 𝗡𝗼-𝗕𝗿𝗮𝗶𝗻𝗲𝗿 Refactoring keeps systems alive, but it is often seen as a cost center: slow, risky, and delaying feature delivery. That is changing. With AI (specifically Cursor) built directly into the refactoring workflow, it is no longer just about cleaning up code. It is about doing it faster, safer, and with measurable business impact. I have never been a fan of the phrase "tech debt". It makes it sound like engineers are the ones who owe something. In reality, the debt belongs to the product or platform. Paying it down almost always improves productivity, quality, maintainability, or all three. Here are four practical ways AI is transforming refactoring:  • 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗨𝗽𝗴𝗿𝗮𝗱𝗲𝘀  AI analyzed the codebase, upgraded critical libraries, handled breaking changes, and generated migration code. Days of manual work and the risk of human error disappeared.  • 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗛𝗮𝗿𝗱𝗲𝗻𝗶𝗻𝗴  Instead of waiting for a fire drill, AI flagged context-specific vulnerabilities such as deprecated authentication methods and provided fixes instantly, strengthening security posture.  • 𝗧𝗮𝗿𝗴𝗲𝘁𝗲𝗱 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀  AI mapped out modularization opportunities such as splitting a monolith and even provided starter code for the new services. This enabled incremental modernization without a risky full rewrite.  • 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗮 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗦𝗮𝗳𝗲𝘁𝘆 𝗡𝗲𝘁  The biggest blocker to refactoring is fear of breaking things. AI generated a suite of unit and integration tests, creating an automated safety net that boosted deployment confidence. The impact:  • Reduced critical vulnerabilities  • Modernized core systems without slowing feature delivery  • Increased developer velocity by freeing engineers to focus on user-facing features Refactoring is not just easier with AI. It now has clear ROI. It shifts from being a necessary evil to a strategic advantage. I am curious. Has your team applied AI in similar modernization work? What has been the most impactful result? #AIinPDLC #Refactoring #TechDebt #DeveloperProductivity #LegacySystems #DigitalTransformation #SoftwareEngineering

Legacy modernization is finally changing. AI is the reason. For years, modernization has been slow, expensive, and risky — largely because it depended on a few SMEs, undocumented logic, and months of reverse engineering before any real progress. That model is breaking. With AI, we are now able to let the system explain itself— analyzing large codebases, mapping dependencies, and uncovering business logic without heavy reliance on individuals. We’ve seen this play out across multiple engagements. In one recent case, we assessed a business-critical legacy platform: ~800,000 lines of code, decades of embedded logic, minimal documentation high SME dependency. Traditionally, this would take several months just to understand the system. Instead, we completed the re-engineering assessment in ~6 weeks — with minimal SME involvement, no disruption to business teams, and a clear modernization roadmap. This fundamentally changes the equation. Modernization is no longer just a technology problem. It is becoming a data and AI-driven problem — where speed increases, risk reduces, and dependency on individuals drops significantly. We’re still early, but the direction is clear. Curious how others are approaching this — where are the biggest bottlenecks in your modernization efforts? #AI #LegacyModernization #DigitalTransformation #CIO #Data

Anthropic just released Claude Mythos, and if you are an enterprise CIO who pushed tech debt and modernization projects down the roadmap, this announcement just made that decision a lot more urgent and risky. Stripe migrated a 50-million-line Ruby codebase in a single day with it, against a two-month manual estimate. AWS launched the model positioned for multi-day asynchronous workloads, where it can run for days on coding work without human intervention. This is the capability now sitting in the market. Here are five implications if your enterprise is running on legacy systems: 1. Security - Every legacy application carrying known vulnerabilities that vendors stopped patching has crossed from technical debt into exposed surface. A model that finds those vulnerabilities at machine speed is now publicly available, and the tools the attacker side will build on top of capabilities like this will not wait for your patching cycle. 2. Economics - Modernization work that used to take a year and a multi-million-dollar program now fits inside a much smaller window. The business case for the projects you have been deferring just cleared on its own, and the math that justified the wait stopped holding up this week. 3. Timing - Mythos is restricted to select partners today, but Fable 5, built on the same architecture, is already publicly available. The runway between controlled access and a commodity capability is short, and it is moving faster than any enterprise modernization roadmap is built to absorb. 4. Diligence - Acquirers, lenders, and auditors will use these capabilities to assess targets in ways they could not six months ago. The legacy systems sitting inside your organization will be visible to the next buyer, the next regulator, and the next insurer in a matter of hours. 5. Talent - The argument that modernization is too hard to staff just lost a lot of its weight. Teams that could not be assembled for a full rebuild can now run a Mythos-class engagement with a much smaller group, which takes the last operational excuse off the table. For enterprise CIOs sitting on legacy systems, you have two practical directions: 1. Full modernization for applications running on tech that vendors have stopped supporting. 2. A tech-stack version upgrade: A focused move to bring software up to currently supported versions and close the most exposed surfaces before broader access to capabilities like Mythos becomes standard. Legacy systems have crossed from productivity drag into the biggest addressable security risk in the enterprise. The window between a vulnerability being discoverable and being actively exploited is going to keep getting shorter. Deferring modernization is no longer a safe default. #claudemythos #legacymodernization