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Measuring Technical Debt Effectively

Anton Kazakov
Anton Kazakov
casavi
Mentorship available

Reevaluating the Notion of Technical Debt

Technical debt is often perceived as the root of all evil in software development. However, this view is worth reconsidering. The concept of technical debt was introduced by Ward Cunningham in 1992. It provides a way to temporarily speed up development by cutting corners, much like taking a loan that must be repaid with interest later. Not all technical debt needs immediate fixing, and understanding this can lead to better decision-making in software projects.

To grasp why not all technical debt is inherently bad, one must first determine its presence in a software project. Key questions to ask include whether the software is easy to analyze and understand, easy to modify, and safe to modify. Additionally, checking whether essential technical requirements like scalability and security are unmet can help identify technical debt.

Different Types of Technical Debt

Technical debt can be divided into two types: maintenance and continuous. Maintenance technical debt relates to elements that slow down changes, whether they are new features or other modifications. It becomes problematic only when changes are required in areas where this debt exists. Continuous technical debt, on the other hand, poses immediate risks as soon as it enters production. It can lead to bugs, downtimes, and security issues, resulting in lost revenue and reputational damage.

Understanding these distinctions helps in determining when technical debt becomes detrimental. Maintenance technical debt is only an issue when modifications are needed, while continuous technical debt requires immediate attention due to its direct impact on operational and business metrics.

Identifying Which Technical Debt to Address

Determining which technical debt needs fixing is crucial. This decision can be guided by various metrics that fall into three categories: heuristic metrics, second-tier metrics, and tech debt interest.

Heuristic metrics are automated and provided by existing tools. They measure factors like cyclomatic complexity, code duplication, and code smells. These metrics can pinpoint hotspots in the codebase but often fail to translate into tangible business impact or effort estimation.

Second-Tier Metrics and Their Role

Second-tier metrics, while not direct technical debt indicators, measure its effects. These include effort split between ad hoc support and features, cycle time for feature tickets, and bug trends. They provide a clear connection to business impact, enabling prioritization of technical debt fixes against new feature development.

Though valuable, these metrics are generic and do not pinpoint specific code areas needing attention. They require interpretation and educated guessing to connect them to the codebase.

Understanding Tech Debt Interest

Tech debt interest, conceptualized by Martin Fowler, focuses on the ticket level of development work. For each ticket, the actual effort and the estimated effort without technical debt are logged. The difference represents the tech debt interest, highlighting areas with significant overhead due to technical debt.

This metric allows for a detailed understanding of technical debt's impact on specific code segments. It facilitates prioritization within the technical debt bucket and provides a clearer ROI calculation for addressing debt.

Implementing an Effective Technical Debt Strategy

To manage technical debt effectively, one must employ a synergy of the discussed metrics. Tech debt interest should be at the core, providing insights into both maintenance and continuous technical debt. Heuristic metrics offer a broad view of codebase health, while second-tier metrics monitor the impact of technical debt elimination efforts.

Collecting tech debt interest involves maintaining thorough work logs and estimating effort without technical debt. This practice, though cumbersome, is essential for informed decision-making and prioritization.

Conclusion: Navigating Technical Debt Wisely

Technical debt is an inevitable part of software development, but not all of it is harmful. By understanding its types and leveraging metrics like tech debt interest, software teams can make informed decisions about which debt to address. This strategic approach ensures that technical debt does not impede progress, allowing teams to focus on delivering valuable features while maintaining code quality.

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From Author:

Hardly any people in Tech like when there's a lot of tech debt. And most of us would like when there's not too much of it. But how do we understand how much exactly we have of it? Where exactly does it sit? Which part of it is actually the most annoying? What would be the benefit for us if we spend time getting rid of it? When it comes to planning how you tackle your tech debt, all these questions deserve answers. Especially when we're asked about the ROI on our efforts to eliminate some annoying legacy stuff and build a new shiny module or service. Also, when we work on tech debt, we do want to tackle the most impactful parts of it first, don't we? This talk is about all of that: how we measure our tech debt, how we interpret the results of these measurements so that they give us the answers to the right questions, and how we guide our decision making with those answers.

This talk has been presented at TechLead Conference 2023, check out the latest edition of this Tech Conference.

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FAQ

The two types of tech debt are maintenance tech debt and continuous tech debt.

Continuous tech debt is immediately problematic as soon as it is deployed to production, leading to issues like bugs, downtime, and security breaches.

Heuristic metrics include cyclomatic code complexity, code duplication, code smells, maintainability index, tech debt ratio, and heuristically detectable security issues.

Second-tier tech debt metrics are metrics influenced by tech debt, such as effort split between ad hoc support and features, cycle time for feature tickets, bug trends, and software uptime.

Tech debt interest is the additional effort spent on tasks due to tech debt, calculated by comparing the effort required with and without tech debt.

The primary topic is measuring tech debt and interpreting the results.

Tech debt is a tool to temporarily speed up development by cutting corners, at the expense of slowing down later.

Teams can collect tech debt interest by logging time spent on tickets, estimating effort without tech debt, and regularly updating this information in their issue tracker.

Second-tier metrics are important because they show the impact of tech debt elimination efforts and are essential for tracking overall team and software health.

The speaker is Anton, a director of engineering at Westing in Germany and a mentor and coach for engineering leaders.

team productivityproductivity
Anton Kazakov
Anton Kazakov
27 min
09 Mar, 2023

Comments

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  • Va Da
    Va Da
    P4
    Link to the mentioned "Estimated Interest" article: https://martinfowler.com/bliki/EstimatedInterest.html
    18:19 Nov 7, 2024
Video Summary and Transcription
This Talk discusses the measurement and interpretation of tech lead, focusing on tech debt. Tech debt is a tool to temporarily speed up development but can have negative consequences if not managed properly. Various tech debt metrics, including heuristic metrics and second-tier metrics, can help identify and manage tech debt. Tech debt interest is crucial for measuring the impact of tech debt and allows for prioritization. It is important to collect and analyze tech debt metrics to ensure software and team health.
Available in Español: Una Guía Rápida y Completa para Medir tu Deuda Técnica y Utilizar los Resultados

1. Introduction

Hi, everyone. My name is Anton, and thank you for having me at TechLeadConf this year. In the next 20, maybe 25 minutes, I'm gonna be talking about measuring tech lead and interpreting the results. I hope this talk will be useful and interesting for you. I lead engineering organizations and mentor other engineering leaders. Feel free to contact me for mentorship or coaching. Let's dive right in.

2. Understanding Tech Debt

Tech debt is not inherently bad but depends on various factors. It is a tool to temporarily speed up development by cutting corners, much like taking a loan. To determine if we have tech debt, we can analyze our software and answer questions such as ease of analysis, modification, and safety. There are two types of tech debt: maintenance tech debt, which slows down changes due to imperfect design, and continuous tech debt, which requires time to keep the application operational. Continuous tech debt becomes bad when deployed to production, leading to bugs, downtime, security breaches, and lost revenue. Therefore, it is important to manage tech debt to avoid negative consequences.
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