How to Turn Support Tickets Into a Modernization Roadmap

When planning a software modernization, teams often start with the technical inventory. Which applications are the oldest? Which frameworks are no longer supported? Which components are hardest to maintain? These are the right questions, and they surely will help you build a modernization roadmap. But we suggest you look at another source of information which is often overlooked: support tickets.

Support history shows where the application creates work in day-to-day operations. Look at recurring bugs, regressions, manual workarounds, and customer-specific fixes. They can reveal parts of the system that are expensive to keep changing and supporting. For a CTO, this can be a way to connect modernization priorities with operational problems.

Key Takeaways

• Support tickets can help identify modernization priorities.
• The oldest part of the application is not always the best place to start.
• Recurring support issues can show deeper architectural problems.
• Manual workarounds are a modernization signal.
• Customer-specific fixes can increase modernization complexity.
• Support data should be combined with technical analysis.
• A modernization roadmap should connect technical priorities with business impact.

Support Tickets are Symptoms

“Invoice calculation is wrong” error is a symptom. The underlying problem could be anything. For example, duplicated business logic, a customer-specific software customization or an integration with another application. In this situation, you’d better ask: what is behind the recurring support work, and what does it tell us about the architecture? This distinction matters because a high ticket count does not automatically make a component a modernization priority.

A heavily used part of the application may generate many minor requests simply because users interact with it every day. Meanwhile, a less frequently used component may require hours of developer investigation every time something goes wrong. You should consider support data together with technical and business context.

What Recurring Support Problems Can Reveal

Some support patterns are particularly useful when assessing a legacy application.

Support pattern	Possible technical signal
Recurring bugs	Structural issue or duplicated logic
Long resolution times	High coupling, unclear dependencies, or limited system knowledge
Regression tickets	Fragile dependencies between components
Manual workarounds	Missing functionality or architectural limitation
One developer repeatedly solving an issue	Concentrated system knowledge
Customer-specific fixes	Accumulated customization

These are signals to investigate. For example, if the same type of production issue appears from time to time, fixing each incident may help you keep the system running. But if the underlying component is also difficult to change and it is connected to several other parts of the application, the recurring support work becomes a topic for a broader modernization discussion.

Look at the Cost of Change

To get a better view of modernization priorities, consider these signals:

support volume + severity + recurrence + change frequency + dependencies

These signals can reveal problems that ticket volume does not show. Consider a module that receives few support tickets but is involved in many customer requests. Every change requires developers to inspect several related components, and a small modification can introduce regressions elsewhere. In this case, the module may not generate much support work but it has a high cost of change. This is why modernization planning should consider both operational pain and change complexity.

Recurring Bugs Can Point to Structural Problems

Some bugs keep coming back because the system makes them easy to introduce. Imagine a business rule implemented in several places. A developer fixes it in one module, but another implementation remains unchanged. A few months later, a similar support ticket appears.

The support team might think that these are separate incidents. But from an architectural perspective, they may be one problem.

We recommend tracing the recurring tickets back to their technical source. Do that before deciding whether to keep fixing the symptom or address the component itself.

Long Resolution Times Can Be More Important Than Ticket Volume

A ticket that takes two hours to resolve may matter more than five tickets that take ten minutes each. If you usually need a lot of time to resolve the issue, it might mean that developers have to spend significant effort understanding how the system behaves before they can make a change. This can happen when:

• business logic is spread across many layers
• dependencies are poorly documented
• similar functionality exists in several applications
• database logic affects application behavior
• ownership of older components is unclear

If the same areas need this kind of investigation, the support history is showing something about the architecture. It is also showing the ongoing cost of keeping that architecture in place.

Manual Workarounds Don’t Always Appear in Support Data

There is another problem with using tickets as the only source of information: users eventually stop reporting problems they already know how to work around. For modernization planning, they are important. These workarounds represent operational cost that the ticket system may not capture.

A useful assessment asks what users and support teams have learned to do manually because the application cannot handle a process directly.

The “Only One Developer Knows How” Problem

A similar pattern appears when the same developer always handles particular support issues. And who knows how much of the system knowledge is concentrated in one person?

Long-lived applications often have behavior that is difficult to reconstruct from the code alone. A developer who has worked with the system for years may know all the small details about how the system works. That knowledge keeps the application running. At the same time, it can make support and modernization dependent on a very small number of people.

A modernization assessment should identify these areas early. They may need more discovery and documentation before the actual modernization work begins.

Customer-Specific Fixes Can Turn Into Architecture

Customer-specific changes are another useful signal, particularly for B2B software products.

A customer asks for a slightly different workflow, and the team makes a small change. Another customer needs a similar exception, so you introduce another modification. Years later, the application may contain many implementations of the same business process. We had such a project.

At that point, the customization has become part of the architecture. This can influence modernization scope. You might plan to move the existing code to a new technology without addressing this fragmentation. But this may reproduce the same maintenance problem in the modernized system.

The Oldest Module is not Necessarily the First One to Modernize

Age alone says little about operational impact. A 15-year-old reporting module that doesn’t generate support work may not need modernization. But a 7-year-old component that affects customer workflows may need much more attention.

Modernization priority should reflect how the software behaves in production and how much effort the business spends maintaining it.

Turning Support Data Into a Modernization Roadmap

Start by mapping recurring support issues to the parts of the application behind them. Then look for patterns in severity, resolution time, recurrence and change frequency. From there, add the architectural context.

Which components are involved? What depends on them? Where is business logic duplicated? Which integrations or databases are affected? Are there manual workarounds or customer-specific versions?

This should give you a fuller picture. For example, you may find that one component generates a moderate number of tickets but is involved in a large proportion of customer changes. Another may be old but stable. A third may generate recurring production incidents because several applications depend on the same fragile integration. These three components can need different modernization strategies. The roadmap might then focus first on the components with the highest combination of operational impact and change complexity, while accounting for their dependencies.

What to Prioritize

A practical modernization assessment can look for areas that:

• generate significant support work
• repeatedly cause production issues
• require lengthy investigation
• rely on manual workarounds
• slow down customer requests
• change frequently
• have many downstream dependencies
• contain fragmented or customer-specific business logic

The more of these signals point to the same part of the system, the more attention it deserves during modernization planning.

Use Support History as Evidence for Modernization Decisions

A modernization roadmap should show which parts of the system need attention and why. It should also show what makes them difficult to maintain, what they depend on, and what needs to be done before modernization can start. Support history can provide part of that evidence.

When recurring support problems are connected with code structure, dependencies, business processes, and change patterns, they become useful input for modernization planning. And this often leads to a different roadmap from one based on application age alone.

How Softacom Can Help

Softacom helps companies assess their legacy Delphi and .NET applications and turn technical findings into a practical modernization roadmap. Our architects can review the codebase, dependencies, business logic, and integrations. They can find the parts of the system that cause support and maintenance issues. We can then connect these findings with business priorities and define a modernization sequence.

For decision-makers, the result is a clearer view of what to modernize, where to start, and why those areas should be part of the roadmap.