Software Development Metrics to Track for Efficient Modernization

Outdated software is one of the weakest links in the chain of technological advancement. Today, more than 40% of enterprise systems have reached end of life or support, while the average age of enterprise software technologies exceeds 12 years. These aging systems raise maintenance costs, absorbing up to 80% of IT budgets, and hinder scalability, automation, and security.

Old software may still run, but accumulate inefficiencies such as slow release cycles, dependency conflicts, integration failures, and high maintenance costs that undermine your business operations. Many companies suffer not because their software stops working, but because they stop measuring how well it’s working. Metrics are the path toward improvement: while organizations usually track business KPIs, they are lacking technical well-being indicators for their legacy systems, which causes modernization gaps. 

Measuring software metrics allows organizations to track performance, reliability, and maintainability. Vital indicators reveal whether the system is evolving or building up technical debt, helping teams avoid gradual degradation and implement proactive modernization.  

We’ll classify IT development metrics for organizations and explain how to interpret and prioritize them for strategic transformation.

Key Software Metrics That Matter in Modern Development

Modern ecosystems are advanced and complex. Cloud-native applications, SaaS platforms, and distributed teams require measurable indicators to retain quality and performance, fulfilling the business tasks.

While the available metrics are multiple, we should highlight two global standards for engineering quality: ISO 25010 and DORA. 

ISO 25010: The quality of a software product

“ISO 25010 defines eight categories of software quality characteristics, each measurable through specific metrics,” explains Andrew Lychuk.

● Functional suitability: The ability to perform required functions accurately and completely.● Performance efficiency: Speed and optimal use of resources.● Compatibility: The ability to integrate or live in synergy with other systems.● Usability: Ease of use and user experience.● Reliability: Stability and failure rate.● Security: Protection against unauthorized access and breaches.● Maintainability: Ease of modification, fixing, and extension.● Portability: The ability to operate in new environments or infrastructures.

From the business angle, ISO 25010 gives a measurable evaluation of software health, not just locating bugs. For instance, by gauging maintainability and performance efficiency, a company can reveal whether their product is prepared for future change.

DORA: The quality of the modernization process

DORA framework stands for DevOps Research & Assessment and evaluates how efficiently engineers can evolve the software product. The indicator includes four metrics: Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recovery, which assess engineering performance and translate it into business responsiveness.

Andrew Lychuk illustrates: “Legacy systems often have very low deployment frequency, quarterly or even yearly, because of fragile architectures and manual testing.”

By contrast, modern pipelines with CI/CD and automation achieve continuous delivery, where gradual change is part of the routine processes.

● Higher deployment frequency means expeditious feature delivery and customer feedback.● Lower change failure rate and shorter MTTR stand for fewer breakdowns and minimized losses in case of incidents.

Strategic interpretation: Software delivery metrics and business value

Your ultimate goal is not the metrics, but the business outcomes. “These metrics are not meant to be pursued equally. They need to be balanced based on your system’s purpose,” says Igor Omelianchuk.

If you have a healthcare platform, you might prioritize reliability and security, with a lower focus on speed. A SaaS product, in turn, may emphasize fast deployment and usability.

Essentially, ISO 25010 and DORA metrics in software engineering provide a holistic view of product quality and delivery capability, to direct your modernization efforts to the areas where they bring the maximum impact.

Most Useful Metrics for Evaluating the Health of Legacy Systems

Legacy systems rarely break spontaneously. They may have gradually degrading elements, affecting various business processes. Therefore, legacy systems’ well-being should be assessed through technical, business, and operational layers. 

Andrew Lychuk explains the structured approach to evaluating legacy health: “How well does the system currently perform its intended business functions? How costly is it to maintain or evolve? And how ready is it for modernization?”

The common software metrics for assessing software health stem from these three diagnostic lenses: functionality, cost, and modernization readiness.

Key metrics in software health evaluation

Use the following list of software metrics to quantify the system’s stability, comprehensibility, security, and adaptability:

● Maintainability Index: Cyclomatic complexity, readability, and test coverage combined into a single value. A low index demonstrates technical debt and growing resources for maintenance.● Reliability and Stability Indicators: Include such metrics as Mean Time Between Failures (MTBF) and incident recurrence rate. Systems with fragile architectures often show frequently repeating issues.● Security Exposure Metrics: Track open vulnerabilities, unsupported frameworks, and patch lag. Delayed updates multiply risks.● Delivery and Change Indicators: These legacy health metrics gauge how efficiently updates move from idea to production by assessing deployment frequency, lead time for changes, and change failure rate. Manual processes and numerous dependencies in legacy systems often lead to low deployment rates and high failure rates.

Software engineering metrics for business and risk alignment

Legacy systems may carry hidden faults that drain budgets and pose business risks. The full story can be understood by considering business alignment indicators. 

● Maintenance-to-Development Cost Ratio: Over 30–40% of the engineering budget going to maintenance signals stagnation and shrinking returns.● Knowledge Concentration Index: Also referred to as the “Bus Factor”, shows the system’s dependency on a few leading engineers who know how it works. ● Vendor Lock-In and Obsolescence Risk: Reliance on outdated technologies proprietary middleware, or unsupported database engines, ties you to a specific vendor, elevates future migration costs, and restrains scalability.● Time to Hire and Onboard New Developers: A practical maintainability indicator. While healthy legacy systems are understandable, efficient, and evolvable, these metrics help to see whether we are maintaining value or incurring cost.

These software development metrics connect engineering effort with business consequences. Together, they create a diagnostic map that helps identify what’s broken, how much the maintenance costs, and what the potential modernization ROI is. 

Metrics That Help Identify Hidden Risks in Outdated Codebases

A minor update in a legacy system can unexpectedly trigger a chain of failures.

Igor Omelianchuk confirms: “The most dangerous systems are not the ones that crash, but the ones that seem stable until a small change brings down an entire process.”

Traditional uptime metrics may show up to 99.9% availability, giving you peace of mind. What they really tell you is how often the system is online. But they don’t uncover how brittle, risky, or costly it is to maintain.

A range of risk-assessing metrics helps you look under the surface.

Security risk indicators

Here are some of the most informative examples of software metrics in security:

● Vulnerability density: The number of security vulnerabilities in a thousand lines of code or in a specific dependency. High values often reflect outdated libraries or frameworks.● Patch lag: The average time from a patch release to its implementation. The longer the time, the higher the risk of a breach, especially in systems with high dependencies. 

Tracking these metrics in software development regularly, businesses can prioritize updates, reduce breach exposure, and preserve security.  

Maintainability and structural fragility metrics

Another set of key metrics for software development includes indicators that reveal the inner structure of the system and reflect how understandable, modular, and resilient the system really is.

● Cyclomatic complexity demonstrates the number of possible code paths. The higher values mean tangled logic that is hard to test or change safely.● Code coupling assesses the interdependence between modules. High coupling witnesses interdependent modules, where modifying one feature can break or damage the other.● Test coverage shows the percentage of code that can be tested automatically. Low test coverage poses regression risk when issues reappear because automated safeguards are absent.

Igor Omelianchuk emphasizes: “The issue exacerbates if you add knowledge concentration. When only two engineers understand the codebase, a single resignation can trigger production downtime.”

Tight coupling and low modularity increase the cost of regression testing and block parallel development, which are core modernization pain points. Without proper modularization and documentation, innovation may completely stall since every update becomes a risk instead of an improvement.

Operational fragility metrics

Even in a functional codebase, operational fragility can silently drain resources. The following key indicators are early warnings:

● Change Failure Rate (CFR). The percentage of software changes that cause downtime or other issues. If many deployments lead to incidents, testing or architecture is unstable.● Mean Time to Recovery (MTTR). The time it takes to identify a failed element and restore the system to the initial state. A long MTTR indicates weak recovery procedures or poor traceability, which undermine business continuity and increase downtime costs.

Therefore, the absence of crashes doesn’t mean a healthy system. Maybe changes are just implemented too rarely to uncover hidden flaws.

Cost–risk tipping point

It’s vital to understand how risk translates directly into business cost. Metrics in software engineering, such as maintenance-to-development cost ratio and compliance findings, show when a legacy system stops being just expensive to maintain and becomes too risky to keep.

When operational costs elevate faster than new feature development, modernization becomes a necessity.

As Andrew Lychuk interprets it: “When 70% of engineering time is spent just to keep the lights on, that’s not a technology story, it’s a business continuity story.”

If a business approaches this breaking point, even incremental fixes will not generate ROI. Tracking software development metrics helps identify modernization urgency. The optimal path, however, is proactive modernization that includes re-architecture, automation, and gradual migration to restore flexibility, remove risks, and control costs.

Performance vs. Reliability: Which Matters More and Why

Systems’ performance and reliability serve different business purposes. A system that runs fast but often fails is useless. A reliable but slow system frustrates users. The right balance between speed and stability depends on the context, yet for most enterprises, reliability is the winner.

“Users will tolerate a slow system longer than one that fails,” explains Andrew Lychuk.

Reliability is the pillar of business resilience in various sectors. It guarantees that payroll systems generate correct payments, medical records remain protected, and financial transactions flow smoothly. Every crash propels costs and deteriorates business reputation.

Reliability metrics

Four primary reliability indicators evaluate system stability and predict business risks:

● Downtime: The time of system unavailability within a certain period.● Incident Frequency: How often failures occur.● Change Failure Rate (CFR): What part of updates causes errors or requires rollback.
● Mean Time to Recovery (MTTR): How long it takes to restore service after a failure.

These software development metrics reflect the system’s predictability, which is paramount in regulated industries, including finance and healthcare. More than just technical stability, it guards compliance, safety, and reputation.

Application development performance metrics

After you’ve taken care of the system’s reliability, your next frontier is performance. The following metrics define how efficiently the system serves users and scales under demand.

● Latency measures the speed of a system's reaction to user requests.● Throughput shows how many operations the system can process per second.
● Response Time fuses these parameters into a unified evaluation of user experience.

Igor Omelianchuk notes: “A system might be reliable, but if each page takes five seconds to load, it’s still failing from a business perspective.”

In B2C sectors, performance has a direct correlation with conversion, engagement, and customer retention. In B2B applications, however, poor performance is also undesired, as it witnesses inefficiency and a competitive weakness.

Strategic sequencing: From reliability to performance

You can attain the highest effect from modernization when it’s performed in the right order. Addressing speed issues in an unreliable system is like decorating an apartment in a house with structural cracks: the hidden risks remain untouched. 

The essence of the proper modernization sequence is aptly summarized by Andrew Lychuk: “First make it dependable, and then make it fast.”

Reliability shapes the foundation for automation, CI/CD pipelines, and safe upgrades. A stable and predictable system allows for performance refinements that don’t cause a mess but bring measurable value. Teams can safely optimize infrastructure, caching, and scalability without endangering business continuity.

Conclusion: From Measuring to Transformative Planning

Programming metrics transform software maintenance from intuitive and reactive to strategic. The key is choosing the right indicators and interpreting them correctly. Then, the abstract concept of overall system health turns into a measurable business insight that can be translated into a distinct action plan.

The true interpretation expertise includes collecting the right metrics and synthesizing them to identify the system’s well-being and modernization readiness. Experienced IT partners match every software metric with business goals to enable proactive modernization: the transformation that removes hidden weaknesses and risks, helping your investments bring the maximum effect. 

Ultimately, the goal of modernization is not reaching certain numbers; it includes making wiser decisions that help keep the system resilient and flexible. In this context, software development performance metrics do more than just describe your software. It plans your path to the future.

Book an expert consultation to select the most impactful metrics for your business and plan insightful modernization.