Tips and Tricks for Measuring and Improving Technical Debt Metrics
As businesses strive to deliver high-quality software products within tight deadlines, effectively managing and measuring technical debt has become critical to software development.
Nowadays, there is a plethora of specific metrics and tools that allow companies to do that. However, to control tech debt and its progress, you need to choose metrics that are the most useful for your specific business and understand how to use them for growth and development.
In this article, we will explore the 7 most useful metrics for technical debt measurement and provide you with insights on how to improve them. We will also outline the best tools for measuring tech debt that will help you optimize the tech debt management workflow.
How Does Technical Debt Impact Your Business?
According to the Software Engineering Efficiency report from Stripe, in 2018, approximately a third of developers’ time was spent addressing technical debt. 52% of respondents believed that technical debt was hindering developer productivity, and 76% stated that it had a negative impact on morale.
In addition, 81% of companies face disruptions to their core business operations and cannot start working with newer technologies because of their technical debt. In fact, 10 – 20% of a company’s technology budget is diverted to repaying the debt.
The impact of technical debt on businesses is an issue that cannot be ignored. Although having some technical debt is acceptable and normal (i.e., the tech debt cannot be completely eliminated), an excessive amount of it can cause a decline in productivity, decreased work quality, constant delays, weakened security, lousy user experience, and budget overruns.
Being proactive with tech debt management can allow organizations to reduce their tech debt from 75% to 25%. One of the first steps in dealing with the problem is using metrics to help quantify it.
7 Metrics for Measuring Your Technical Debt and How to Improve Them
A study from 2018 found that only 7.2% of organizations methodically track technical debt, and only 26% use a tool for it. However, those who actively manage and reduce technical debt achieve at least 50% faster service delivery times to the business, along with many other benefits. This is why it is essential to know how to measure technical debt and how to choose the most useful metrics based on your business’s individual needs and objectives.
Some metrics we suggest to monitor are defect ratio, debt index, lead time, code coverage, tech debt ratio, change failure rate, and the number of failed CD and CI.
Defect Ratio (New Bugs vs. Closed Bugs)
The defect ratio metric compares the number of newly discovered bugs or errors reported during a specific period (i.e., usually a sprint or a release cycle) to the number of bugs successfully resolved or closed within the same timeframe.
Defect Ratio = Number of New Bugs / Number of Closed Bugs
A low ratio indicates that the IT team successfully and effectively addressed the issues. A high defect ratio, on the other hand, suggests that new bugs are being introduced faster than they are being fixed, potentially indicating issues with quality or development processes.
Auditing the current code and processes, you might realize there is a problem in QA processes, the code needs refactoring, or something else is the cause. Fixing those problems will prevent the high defect ratio problem long-term.Serhii Kopeikin, COO at Maven Solutions
Lead time measures the time between the commitment to the task and its release. The metric is measured in units of time, such as hours, days, or weeks, and is beneficial to understanding the efficiency and speed of solution delivery.
Long lead times can make legacy software or products obsolete even before they are delivered, increase costs, and reduce customer satisfaction. In order to improve the metric, it is important to identify bottlenecks in the processes and try to automate them. The more automation there is, the less time is wasted and the more value is delivered.
The debt index is calculated by dividing the total technical debt by some measure of the system’s scale, such as the size or complexity of the codebase. The specific formula for calculating the debt index may vary depending on the methodology or tool used.
For example, you can calculate the debt index by dividing the total technical debt (e.g., measured in hours or story points) by the codebase size (e.g., lines of code or function points). This ratio will provide a relative measure of technical debt per unit of code size or complexity.
If reducing the debt index is one of the company’s KPIs, the advice is to analyze this metric alongside other essential indicators and plan to modernize legacy applications, choosing the most suitable approach for it.
The code coverage metric assesses the proportion or percentage of code that is tested by automated tests. It is typically calculated by measuring the number of code lines or code blocks executed during the test suite divided by the total number of lines or blocks in the codebase. The result is represented as a percentage, indicating the coverage level achieved by the tests.
A higher coverage percentage indicates that a larger code portion has been tested and that fewer bugs or untested scenarios are likely to occur later. However, it is better to determine which percentage of code coverage will be considered successful for every particular business before beginning the testing. A good target number could be somewhere around 70%.
Technical Debt Ratio (TDR)
The Technical Debt Ratio (TDR) measures the ratio between the estimated effort required to address existing technical debt and the estimated effort required to develop new features or functionality. It shows how much it will take to fix the problems compared to building new functionalities.
The technical debt ratio formula is:
(Remediation Cost ÷ Development Cost) × 100 = TDR
For example, if the estimated effort to address technical debt is 100 hours and the estimated effort to develop new features is 500 hours, the TDR would be 0.2 or 20%. This means that 20% of the development effort is spent on resolving technical debt, while 80% is dedicated to new feature development.
TDR can be measured in both monetary and time value – whichever is more beneficial for particular goals.
In order to improve the TDR, it is better to focus 100% of resources on dealing with technical debt instead of developing new features for a period of time. The business might also decide to implement one of the application modernization approaches to receive tech debt elimination.
Change Failure Rate (CFR)
Change Failure Rate or CFR is used to assess the quality of change management processes and identify areas that require improvement.
Change failure rate (CFR) = (Number of Failed Changes / Total Number of Changes) * 100
A high CFR indicates that changes are causing disruptions or failures, which can lead to negative impacts on system stability, user experience, and overall business operations. On the other hand, a low CFR suggests that the organization has effective change management practices, which result in a higher success rate for implemented changes.
Lowering the CFR requires a complex approach. We advise conducting regular retrospectives on the reasons for failures in order to help determine and eliminate the bottlenecks of processes.
A number of failed CD and CI Events
The number of failed CD (Continuous Delivery) and CI (Continuous Integration) events is the count of instances where the automated processes associated with CD and CI have failed during the software development lifecycle. Tracking this measure helps organizations assess the health and effectiveness of their automation processes.
A high number of failures may indicate issues with code quality, inadequate testing, or problems with the deployment infrastructure. It highlights areas where improvements can be made to enhance the reliability and efficiency of the software delivery pipeline.
Improving the metric requires inspections and retrospectives. Describing and analyzing the problems helps implement changes that will prevent a high rate of failures.
The Best Tools for Measuring Technical Debt
As previously mentioned, only 26% of organizations use a tool to track technical debt. However, knowing where and how to track technical debt gives these businesses a great advantage in automating their processes, organizing a better workflow, tracking tech debt progress in real-time, and much more. This is why it is important to pay attention to the platforms and programs that can help businesses ease the process.
The tools we will talk about further are Stepsize, SonarQube, Teamscale, Checkstyle, and Mend.io.
Stepsize allows engineers to track, collaborate, and prioritize technical debt from the IDE without replacing Jira. With it, IT teams can find tech debt issues from the code itself “without digging through a backlog.”
Stepsize’s CollabGPT product help engineers increase their productivity and efficiency by unifying information from Slack, Jira, and GitHub and providing insights and summaries. This data allows developers to gain more clarity and visibility into the tech debt, making better-informed decisions as a consequence.
SonarQube is an open-source platform used for continuous code quality inspection and static code analysis. It helps developers highlight bugs and code smells, delivering data that is valuable for tracking.
It is important to remember that the tool helps maintain code quality, but manual code reviews and good programming practices should also be part of the development process.
Teamscale offers a range of features that assist development teams in analyzing, monitoring, and improving their software projects, delivering the information through visualizations.
Teamscale can give real-time feedback, conduct a Test Gap Analysis, deliver metrics data in the form of dashboards, and more. Every developer can configure private dashboards or share them with the team.
Checkstyle is another open-source code analysis tool that helps programmers write Java code that is compatible with coding standards. Automating the process can speed up the time spent on tasks and free developers from having to manually check the code. Thus, there is less likelihood of human error and more time for professionals to work on creating new features.
Mend.io (formerly WhiteSource) offers a comprehensive platform that is specifically designed to help organizations effectively manage the use of open-source software, ensuring compliance, security, and overall visibility.
By analyzing the software’s dependencies, WhiteSource can provide valuable insights into those components’ licenses, security vulnerabilities, and quality. This enables development teams to discover risks and vulnerabilities, which can help to prevent tech debt occurrence, among other benefits.
Measuring and improving technical debt metrics is one of the first and most important ways to deal with it, eliminating the problems of business operations disruptions, budget drainings, a decline in productivity, decreased work quality, weakened security, and many more.
Some tech debt metrics that would be beneficial to pay attention to are defect ratio, lead time, debt index, code coverage, technical debt ratio, change failure rate, and the number of failed CD/CI. Tools like Stepsize, SonarQube, Teamscale, Checkstyle, and Mend.io can be helpful in measuring tech debt and dealing with the issue.
If your company is considering hiring an external service provider to help decrease the technical debt problem, Maven Solutions can get you the result you aim for. With 13+ years of experience in different niches (e.g., Travel, Fashion, Goods for children, Food, Logistics, and Branded products) and sizes (e.g., from startups to Fortune 500 and the Inc.5000), we know how to deal with the tech debt. Contact us to discuss your business needs.
What metrics measure technical debt?
There is a wide variety of metrics that can be used to measure technical debt, and the specific ones should be chosen based on the individual priorities of every business. However, we recommend paying attention to defect ratio, lead time, debt index, code coverage, technical debt ratio, change failure rate, and the number of failed CD/CI.
How do you keep track of technical debt?
To keep track of the technical debt, you can document each instance of it, use the aforementioned tools and metrics, use code annotations or comments within the codebase to highlight areas affected by technical debt, and make code reviews a regular practice.
Why measure technical debt and how to use it in investment calculations?
Measuring technical debt is essential for awareness and visibility of the current state of the codebase, risk mitigation, and decision-making on trade-offs between new feature development and debt reduction efforts. Technical debt metrics can help estimate the cost of reducing or eliminating specific debt items, assessing the potential ROI, and setting investment priorities.
What are the main KPIs in technical debt management?
Any metric can turn into a KPI if it is the organization’s priority. Some KPIs used in technical debt management are technical debt ratio (TDR), code duplication, test coverage, mean time to resolve (MTTR), etc.