Measuring and Managing Technical Debt to Drive Innovation
With the rapid pace of technological change, technical debt is something almost every company battles. And while having a little is acceptable, the 20-40% that the average company keeps is crippling performance and stumping innovation. Companies that prioritize reducing technical debt have the resources available to grow the business to remain competitive—those that don’t face greater risks, architecture complexity, and costs, as evidenced by the recent Southwest Airlines and FAA catastrophes
Some try to avoid technical debt, putting off dealing with it until next year or rely on manual efforts that are slow, complex, costly and inaccurate. It is common to try to use outdated static analysis tools in hopes they will reveal the applications draining resources, yet few of those approaches are accurate, data-driven, or paint the full picture. But there is an answer, one that comes with the aid of artificial intelligence. With an AI-based tool, organizations can automatically identify technical debt and do something about it by modernizing legacy applications so they’re functional in the cloud.
What Is Technical Debt?
Technical debt is defined slightly differently depending on who you ask, but the Enterprisers Project explains it well by saying, “Technical debt—or code debt—is the consequence of software development decisions that result in prioritizing speed or release over the [most] well-designed code. It is often the result of using quick fixes and patches rather than full-scale solutions.” Just as with financial debt, companies accumulate technical debt when they put short-term wants over long-term implications.
Why Is Measuring Technical Debt Important?
Innovation is key to growth, profitability and adapting to constant change, and it requires human and capital resources. When those resources are allocated to fixing problems and designing workarounds to get more life out of legacy applications, there is little left for value-based initiatives that build market share and drive revenue. Measuring technical debt is the first step, and some say it’s elusive and difficult, but AI and machine learning are modern approaches that unravel dependencies so technical debt is a tangible metric.
Technical Debt Resources
The Top 5 Reasons Technical Debt Accumulates in Your Business Applications
Learn the five major sources of technical debt, how to approach managing technical debt, and techniques proven to keep you on the right track. You’ll also be prepared to deal with anticipated pushback from stakeholders who need more than theory to take on the cost, risk and complexity of modernization.
Eliminating Technical Debt: Where to Start
Modernizing legacy applications in an effort to reduce technical debt is a big project, one that many organizations avoid because of its scope. But once you get buy-in, where do you begin? This article will walk through the journey from assessing your current state through rearchitecting, plus the #1 tool you can use to accurately measure technical debt to make the process significantly faster.
Why Application Modernization Is Now a Top Priority for CIOs
Hear from a Chief Engineer and Founder of Engineering Capital on why modernizing aging monolithic applications has shifted from a nice-to-have initiative to a must-have priority for CIOs. You’ll learn how they approached technical debt and what tool they used to speed the process while reducing cost and risk.
How AI Is Simplifying Application Assessments to Identify, Measure and Analyze Technical Debt
Gartner says companies that excel at managing technical debt can expect at least 50% faster delivery times to the business. It’s amazing what’s possible when resources are focused on innovation rather than keeping the lights on. Given an AI-based tool to address and manage technical debt and IT staff become superheroes, capable of taking the organization to another level of efficiency and competitiveness.
5 Steps to Eliminate Technical Debt
The more diverse the technical stack, the more complex the dependencies and the more challenging it is to eliminate technical debt. But challenging doesn’t mean impossible, and the payoff is worth the effort.
Follow these five steps, and technical debt can be a thing of the past.
- Identify business-critical and business-necessary applications (and leave the rest alone).
- Analyze and measure technical debt.
- Decide which applications to migrate as-is and which to modernize, then prioritize.
- Use the Strangler Fig Pattern to replace monolithic functions with microservices one by one to reduce risk.
- Test one microservice at a time until the entire application is restructured or key functionality is replicated; then, you can retire the monolith.
AI to the Rescue
Because monolithic applications are organized as a single entity with hidden dependencies, manipulating one line of code can unintentionally impact other facets of the application; therefore, understanding those dependencies is critical.
Organizations must have the right analysis tool to assess the legacy application portfolio because doing it manually is typically out of the question and why so many companies fail to get stakeholder approval. Stakeholders have a point. Just look at these red-flag stats:
- 79% of app modernization efforts fail
- The average cost of a modernization project is $1.5M
- The average modernization project takes 16 months
Fortunately, AI and machine learning are making the process much easier, faster and more reliable.
Only an AI-enabled platform can rapidly analyze entire portfolios in a matter of hours and put you on a direct path to modernization. It can identify exposed and hidden dependencies per application to assess the true technical debt and measure refactoring risk associated with each application. With reliable insights in hand, IT leaders are well-equipped to justify resource investment. Even better, this type of platform is capable of refactoring complex monolithic applications into microservices automatically to speed modernization efforts without all the risks.
Strategies for Reducing Technical Debt
Technical Debt—A Guide for Frustrated Software Architects and CIOs
Despite the desire to modernize applications, why do the majority of such initiatives end in failure? Read about how technical debt hampers application modernization initiatives, the true cost of technical debt across the organization, and how to accurately calculate technical debt and build a case for reducing it from the portfolio.
Application Modernization: An 11-Step Strategic Guide for Software Architects
Application modernization is the primary solution to reducing technical debt, but tackling it isn’t always easy for architects. Discover the best practices software architects can use to improve outcomes in their transformation efforts.
How to Use the Strangler Fig Pattern for Application Modernization
Learn more about the Strangler Fig Pattern, how it works and why it’s so beneficial for application modernization to reduce technical debt. Walk away with best practices and how to implement the Strangler Fig Pattern with vFunction and AWS Refactor Spaces.