AI/ML Archives

How AI is transforming tech debt management

Every enterprise operates with some degree of tech debt. With millions of lines of legacy code powering critical operations, tech debt is inevitable—eventually, all code becomes outdated. While tech debt isn’t inherently “bad,” it can limit an enterprise’s ability to adapt, innovate, and remain competitive.Addressing tech debt isn’t just about identifying issues; it’s about managing them across an enterprise modernization initiative with orchestration, insight, and accountability. That’s where platforms like MLP (Modernization Lifecycle Platform) become essential.

AI-powered modernization solutions offer advanced capabilities to enhance the assessment and management of tech debt, such as:

Automated code analysis

AI-powered tools can automatically analyze codebases to detect code smells, architectural issues, and other indicators of tech debt. For example, CodeScene uses machine learning algorithms to identify patterns in version control data, highlighting hotspots, i.e., code areas frequently modified and may require attention. This behavioral code analysis helps prioritize tech debt mitigation efforts.

Predictive maintenance

AI can predict which parts of the code will likely cause future issues by analyzing historical data and code evolution patterns. This foresight enables teams to proactively address potential problems before they escalate, effectively managing tech debt.

Prioritization of refactoring efforts

AI can assess the impact of tech debt on various aspects of software performance and maintainability, helping teams prioritize refactoring efforts based on factors like code complexity, defect density, and contribution to business goals. Tools like NDepend provide metrics and visualizations that assist in understanding and managing tech debt within .NET applications.

Estimation of remediation costs

AI can estimate the effort required to address specific tech debt items, enabling better planning and resource allocation. The SQALE method, for instance, offers a framework for assessing source code quality and estimating the remediation costs associated with tech debt.

Continuous monitoring and reporting

AI-driven tools can continuously monitor codebases for new tech debt instances, providing developers real-time feedback. This continuous integration ensures that tech debt is managed proactively, preventing its accumulation over time.

These AI capabilities are most effective when deployed within a unified modernization framework. MLP provides the end-to-end infrastructure to integrate AI into each phase of tech debt remediation, from initial discovery and impact assessment to automated code transformation and final validation. By embedding AI tooling into the MLP workflow, organizations can move beyond static analysis to execute modernization plans with measurable outcomes and full traceability.

Platforms like MLP make this integration actionable by supporting AI-assisted analysis and rule-based automation across diverse environments, including mainframe, midrange, and distributed systems. MLP’s ability to coordinate modernization assets, automate repetitive tasks, and generate audit trails gives enterprises a practical path to address tech debt while aligning modernization efforts with business priorities.

By integrating AI into the software development lifecycle, organizations can significantly enhance their ability to identify, assess, and manage tech debt. This will lead to more maintainable codebases, efficient development processes, and a stronger foundation for future innovation.

GenAI and code migration: what’s changing

In the rapidly evolving technology landscape, staying ahead requires not just incremental improvements but transformative leaps. As organizations strive to modernize legacy systems and migrate to more efficient and scalable architectures, the advent of Generative AI (GenAI) is poised to redefine the process.

Over the past several weeks, our discussions have centered on how this cutting-edge technology will reshape our industry and how to harness this power within our business, service offerings, and as part of MLP. Here are six ways we see GenAI unlocking new possibilities for accelerating code migration, enhancing compatibility, and ensuring smoother transitions to modern frameworks.

Enhanced analytics and pattern recognition:

GenAI offers significant benefits for code migration by speeding up the understanding and analysis of legacy systems. It excels at recognizing patterns in code, which are essential for identifying and translating complex structures and dependencies.

Efficiency in code migration:

AI tools can handle time-consuming and repetitive tasks more effectively than humans, especially those requiring detailed pattern recognition. This includes activities like identifying UI patterns and dependencies in code, which traditionally required significant manual effort.

Evolution of migration processes:

The traditional approach of manually creating and maintaining migration libraries and tools is becoming obsolete. AI can automate and streamline these processes, reducing the need for specialized knowledge and extensive manual coding.

Impact on the industry:

GenAI’s rapid improvements and capabilities are poised to change how companies approach code migration. While current migration methods involve custom tools and manual processes, AI can provide more dynamic, on-demand solutions. This shift might lead to significant changes in business models and competitive dynamics in the industry.

Future directions:

Companies that adapt to AI-driven methods will have a competitive advantage. GenAI allows for more sophisticated transformation beyond mere code translation, supporting modern architectures like microservices and micro frontends. The emphasis will shift from simply rewriting code to transforming entire systems to meet contemporary standards and user experiences.

Strategic investment:

As AI capabilities advance, companies are expected to invest more strategically in modernization efforts. AI-driven solutions promise not only cost savings but also the creation of innovative, competitive technology that provides significant business value.

Our discussions highlight that while GenAI will democratize many aspects of code migration, the key to success will be leveraging AI to enhance the complexity and quality of transformations rather than just focusing on code translation.

Contact us if you’re curious about how GenAI can help you modernize your legacy application. We’re happy to discuss your particular needs and help you determine how best to get started.

Facebook TransCoder: a migration panacea or a mirage?

Last year Facebook announced TransCoder, a tool that converts code from one programming language to another. Like many companies, Facebook also has legacy code that runs critical features and functionality of their platform. They also have billions of active users. It’s no wonder they chose the automation approach for migrating their legacy code to more modern technologies. With this approach, Facebook can preserve its original investment and reduce the risk of significant business disruptions that the proverbial, brute-force rewrite would otherwise bring.

Facebook TransCoder Flow Image — Source: Facebook AI Blog

TransCoder can help modernize legacy systems; however, the devil is always in the details when trying to bring the migrated code to production quality, release the migrated legacy application into production, and retire it.

Any machine learning translation tool can only get the complete migration of an application so far. If Facebook’s TransCoder can translate 90% of the application code, one line out of every ten still needs a software developer’s attention.

For an application with ten million lines of code, one million lines of code would need to be hand-written with production quality.

A manual rewrite of 10% of a large application may take years. In fact, the translated code may never see a production environment. Even with Facebook’s size, virtually unlimited resources, and access to the world’s best talent, the company will still need to manage the entire software migration lifecycle and all of the pieces that it takes to bring the new code into production.

Modernization is more than just code translation

Machine-driven migration tools from source to target programming languages play a crucial role in achieving successful modernization projects. These tools are akin to best-of-breed compilers and their role in greenfield application development. Yes, we need a good compiler, but without the well-established best practices of DevOps, no compiler by itself can ensure the successful completion of a software development project.

What will it take to migrate a large and often complex body of legacy code that runs a critical aspect of the business to a modern technology platform and release it into production without any operational disruptions or development freezes?

This particular challenge has been the Achilles’ heel of every modernization project. No migration tools, including the TransCoder, make any attempt to even mention it or, let alone address it.

Tools like TransCoder are often positioned as “auto-magic.” Buy a piece of AI software, and *poof* all of the migration work is done in a few keystrokes. But a programmer cannot take a COBOL program, wave an AI wand over it, and turn it into microservices or properly architected modern-day application. Right now, AI tools are decades away from being able to transform legacy applications in this manner.

Migration tools inside a modernization process

Migration tools such as TransCoder are just pieces of the chain of moving parts needed to run a well-oiled machine of an otherwise complex modernization process. Therefore, the real value is in integrating such tools inside the entire modernization lifecycle to achieve the kind of an assembly line that is needed to make a complex modernization manageable in terms of process and predictable in terms of time and cost.

No single automation tool is a silver bullet for a modernization project, and we should know. We’ve spent 25+ years modernizing legacy applications, building and using our proprietary migration tools. When we finally managed to integrate the source code migration tools into an entire modernization process, our clients saw considerable gains in code quality, efficiency, and affordability.

Our Modernization Lifecycle Platform (MLP) supports the entire modernization lifecycle: from analysis and planning to transformation and remediation; from build and deployment to testing and production release. It applies the same systematic, iterative, and automation-driven modernization processes to produce production-ready, modernized applications. It is compatible with any translation libraries or rule-sets, no matter the source or target programming language, platform, or framework. By automating the complete modernization process where a tool like TransCoder can be integrated into as part of an entire assembly line, the MLP platform:

Saves thousands of hours of manual effort
Reduces the time and cost of a modernization by 90% compared to traditional approaches
Is 100% automation driven yielding predictable outcomes
Ensures 100% functional equivalence
Eliminates the risk of introducing unexpected regressions or random defects
Provides complete transparency and interoperability for all stakeholders

Like Facebook’s TransCoder, new tools are emerging to take on challenges evident in legacy application modernizations, but they are limited in and of themselves.

An integrated platform that facilitates an automated, reliable, and transparent modernization while ensuring 100% functional equivalence with no operational interruptions is needed to take the migrated application into production.

MLP delivers what TransCoder only promises.

FinovateFall 2019: financial services modernization urgency

FinovateFall in September is always one of the most highly anticipated shows for the FinTech industry. With over 1,600 key influencers, 60+ live demos, and 120+ expert speakers, it’s four intense days of networking, learning, and strategizing for the future.

While this year’s major themes centered around big data, analytics, AI, customer experience (CX), and digital transformation, an underlying buzz said change is in the air. The anticipated change isn’t just about the new tech, which is always exciting, but the shrinking generational digital divide. We are entering unprecedented times where the largest wealth transfer in history will start taking place, and the younger generation is beyond just digitally tuned-in—they don’t know any other way of life.

After reflecting on the show, we’ve distilled what we learned to these four takeaways:

1. Disruption is real and is happening.

We’ve been hearing for some time that the financial services space will be disrupted by the digital economy, but now that time truly seems to be here. Digital-only banks such as Ally are gaining market share, big tech giants like Amazon and Apple have launched digital financial service offerings, and traditional financial institutions such as US Bank are going beyond simple digital strategies to develop highly-personalized, smart mobile banking applications.

“When it comes to financial systems, there are a variety of major threats to the status quo,” wrote Greg Palmer, Vice President of Finovate and Master of Ceremonies for FinovateFall 2019 in his recent blog. “But an alarming number of FIs [financial institutions] are falling into the same trap…they aren’t making the ‘responsible’ or ‘grown up’ decisions right now that will make their lives easier in the future. Instead, they are waiting for the next big shock to force them to.”

The big message from the conference was either innovate and disrupt your financial services company or be disrupted by someone else. Disruption is happening.

2. Company culture can make or break your initiatives.

Although FinovateFall 2019 showcased new tech, many of the session speakers mentioned company culture as a critical factor in transformational initiatives. Cultures must become more agile, innovative, and customer centric to stay competitive. In “Building a Culture of Innovation,” panelists from HSBC, Amazon, and Ondot Systems stressed the role leaders play in a company’s cultural change. It starts at the top with clearly defining and articulating the vision, aligning staff and champions, and proactively managing the process.

They also said to look at examples outside of financial services for inspiration. Companies in other industries have built centers of excellence and innovation labs to help foster creativity, and these lessons can be applied to financial services. If customers are having simple, impactful digital experience with non-financial services industries, they will expect the same from their banking and insurance institutions. Panelists warned not to outright mimic the competition or other industries, however. Take the time to understand what your company’s end customers are seeking, and what makes sense for your company’s team and your company’s brand. Your culture can be a competitive differentiator and driver of transformation.

3. New technology is an enabler of, not a substitute for, sound strategy.

It’s easy to get caught up in the latest and greatest technology as the solution to digital transformation and digital customer experience. But technology is only an enabler of a sound strategy and a thorough understanding of what your current and future customers want and need from your institution.

In the session “Delivering the Next Generation in Customized Customer Experience,” executives from Bank of America, Citi, and Northwestern Mutual discussed the importance of investing in digital experiences that are personalized to the varying needs of customers spanning generations. They recommend mirroring the digital experiences with which customers are already familiar and integrating services into those channels that customers are already using. They also reminded us not to discount the influence of Millennials, as they are promoting change and increasing adoption of digital experiences within older generations.

4. Turn legacy systems from roadblocks to facilitators of innovation

For large, well-established, and decades-old brands, legacy technology is just a fact of life. A few sessions addressed using APIs to extract data from legacy systems to power analytics, dashboards, reports, and even new services. While these API transformation programs can potentially unlock some of the value of the legacy systems, the sessions also included discussions about modernizing legacy systems as part of your overall technology strategy.

The reality is that systems built on aged or legacy technology can be vulnerable to cyberattacks. (Just ask Equifax.) Yet some of these in-house applications still operate core business operations. Rather than bolting on net new technology, develop a plan to modernize these critical legacy applications to help enable the digital experiences that consumers want while reducing security risks.

Thanks to @finovate for a fantastic show. See you next year!

About Synchrony Systems

At Synchrony Systems, we help financial services companies transform legacy, in-house applications to modern technologies while preserving business-critical functionality. Using the world’s only Modernization Lifecycle Platform, Synchrony Systems provides an automated, reliable, and transparent modernization while ensuring 100% functional equivalency with no operational interruptions. And with our continuous upgrades, your in-house applications will never fall behind again.

Slavik Zorin featured in SIIA’s Vision from the Top

Greenwich, CT (November 12 2018) — Synchrony Systems, Inc., a leader in legacy application modernizations, today announced that their CEO Slavik Zorin was the featured executive in the Software and Information Industry Association’s (SIIA) Vision from the Top. This program gives members a glimpse into what drove the success of these industry leaders. At the end of the year, all interviews will be released in the form of an eBook.

An excerpt from the interview can be found below. The full interview can be found here on the SIIA website.

Interview Excerpt:

Jennifer Carl: Over the past twenty years what advances have you seen in the modernization of software?

Slavik Zorin: If I look back over 20 years and think about the trends, it has really all started with the mainframe or the “big iron”, where all application software was developed, maintained and run, all in one single place. Over time, this monolithic computing power has been supplemented by more granular and distributed computing power, namely the personal computers and network operating systems. This marked the beginning of an architectural revolution, away from large and monolithic systems, and towards highly distributed and scalable systems that are more flexible, and easier to develop and deploy – what is today’s microservices architecture.

As the rate of change in technological advances continues to accelerate, especially as we look at technologies such as Big Data and AI, we are beginning to approach the age when software will be able upgrade and modernize itself. This will not only become transformational, but also disruptive to the service-driven businesses and migration companies alike. As this future begins to unfold, Synchrony’s MLP platform must play an important role in managing a large number of application modernizations across diverse programming languages and platforms, incorporating the required knowledge and systematic process in order to achieve a frictionless and continuous modernization of application software.

Jennifer Carl: You are a technologist at heart. What is sparking your interested at the moment, and how does it apply to Synchrony?

Slavik Zorin: Lots of things interest me in the industry. IoT, for example, is extremely interesting and incredibly empowering evolution of technology in terms of the benefits it’s going to deliver to the world. We are going to be able to monitor everything—agriculture, logistics, automobiles, medicine, our homes, our health, you name it… But It’s not just about the monitoring; it’s the massive amount of real-time data collected from these miniature IoT devices, and the harnessing of this data that will foster progress, global prosperity, and change the way we interact with each other and the world.

But it is the intersection of machine learning (ML) or AI, if you prefer that term, accompanied by the big data analytics, where we see the possibilities for Synchrony Systems. As the MLP platform and its ecosystem expands to manage hundreds and even thousands of modernizations, the knowledge of how systems are built will grow geometrically making the perfect place to apply ML. Imagine taking a monolithic system and having a really powerful learning and inference engine intelligently extract business rules, APIs, and turn a monolithic application architecture into a scalable microservices architecture.

Blockchain is another disrupting technology that might have interesting applications in our business. A well understood measurement of software applications is lines of code (aka LOC). LOC is also the way we compute the underlying intrinsic value when transforming LOC from one programming environment to another. We can imagine a digital currency based on LOC that can emerge and be used to transact within our ecosystem. This is something we are keeping tabs on and might play a role in creating.

The full interview can be found here on the SIIA website.