Managing Technical Debt And ‘Invisible Work’ In Scrum Projects

What is Technical Debt and Why Does it Matter?

Technical debt refers to the implied cost of additional work caused by choosing an easy software solution now instead of using a better approach that would take longer. It is a metaphorical debt that accrues interest in the form of extra effort needed to maintain and extend the quick-and-dirty solution over time. If left unaddressed, technical debt accumulates and eventually reaches a tipping point that requires significant refactoring, delaying new features for customers and placing time pressure on developers.

Some common sources of technical debt include duct tape fixes, lack of automated tests, flawed architectural decisions, and shifting priorities that lead teams to take shortcuts to release features faster in the short-term. The incremental long-term time and cost penalty of accumulating technical debt is often invisible at first. Over time it starts to slow velocity as a consequence of prioritizing expedient solutions over technical excellence.

Allowing too much technical debt hurts developer productivity in the long run, decreases system maintainability, limits business agility, and continually forces teams to go slower in order to pay back interest. However, not all technical debt needs immediate repayment. Strategically taking on some technical debt can be financially viable if it accelerates value to customers in alignment with business priorities. The key is intentional and strategic planning to balance short-term wins with long-term maintainability.

Common Causes of Technical Debt in Scrum Projects

Quick Fixes and Duct Tape Solutions

On fast-moving Scrum projects, developers sometimes apply quick fixes and duct tape solutions to get something released sooner without fully considering long-term implications. These rapid workarounds often involve shortcuts such as copying and pasting code or adding temporary hooks between components instead of integrating systems the right way.

While expedient in the short-term, these technical debt “IOUs” accumulate speed bumps over time in the form of brittle components prone to bugs. Quick-fix solutions generally lack adequate test coverage and documentation. They typically degrade system architecture since downstream dependencies get added before underlying components have stabilized. What feels like marginally faster delivery velocity today substantially slows enhancements tomorrow.

Lack of Automated Testing

Insufficient automated test coverage is perhaps the most prevalent source of technical debt for Scrum teams. When teams race to deliver business value quickly by not building proper regression and unit testing harnesses they inadvertently sow quality issues that reduce velocity later.

Missing or inadequate test automation eventually leads to time-consuming manual testing cycles with frequent defect-fixing turnover. These quality assurance inefficiencies slow throughput over time for subsequent user stories. Delivering code faster now by not automating testing causes slower deliveries later when more test debt has piled up.

Poor Architecture Decisions

Early architecture choices have cascading impacts on downstream agility and sustainability. At project inception or the transition to Scrum, teams often retain legacy code assets or select foundational frameworks based on past habits versus evaluating tradeoffs of alternatives aligned to evolving business objectives.

Over-optimization for certain attributes leads to architectural drag over time, such as heavily favoring performance versus modularity. Early technology selections might flex initially through makeshift additions and workarounds, but will slow development as underlying technical debt manifests in higher ongoing maintenance costs.

Switching Priorities and Changing Requirements

Dynamic business environments inherently involve shifting priorities and changing requirements. When Scrum teams jump frequently to new features, incomplete work left behind adds technical debt in the form of temporary stubs waiting to get connected, components with assumptions about delayed design details, and refactoring postponed for another day.

These unfinished transitions between user stories accumulate drag inducing workarounds, shortcuts, and technical impediments. Changing requirements mid-sprint often derails architectural runway, trading velocity now for insurmountable complexity later when all those halfway measures require consolidation.

Strategies to Manage Technical Debt

Establish Coding Standards and Guidelines

Creating and enforcing consistent coding standards, architecture guidelines, and development principles is essential for managing technical debt. Well-defined technical expectations based on industry best practices avoid the accumulation of messy, idiosyncratic software. Code reviews, static code analysis, and peer oversight ensures compliance.

Style guides and system architecture rules also facilitate understanding across the team. Onboarding new team members ramps up quicker when all code shares consistent designs, interfaces, patterns, and organization. Standardizing early on prevents divergent language dialects that default to the lowest common denominator.

Refactor Legacy Code During Each Sprint

Trying to rewrite all legacy code at once guarantees failure. The key is incremental improvements through patience and discipline. By working refactoring into each sprint teams systematically pay back technical debt in digestible installments.

Setting measurable goals for improving test coverage or reducing code smells over multiple iterations avoids unrealistic expectations. Consistently refactoring 2-5% of the codebase sprint-over-sprint earns compounding interest in the form of a much cleaner and more extensible codebase after a year or two.

Prioritize Technical Tasks in the Backlog

Clearly capturing technical work needs as user stories within the backlog makes space for infrastructure enhancements, architecture scaling, performance improvements, and other refactoring. Prioritizing those items alongside new features transparently balances business priorities and technical needs.

By tracking technical debt repayment and infrastructure projects in the same backlog as app capabilities, technical excellence receives equal visibility and consideration during sprint planning. Just like product features, technical tasks get sized, estimated, and ranked against other work based on business value versus development costs.

Include Technical Debt Analysis in Sprint Reviews

Treating technical debt seriously means assessing those factors during sprint reviews before planning the next iteration. Code coverage trends, defect rates, system internals, test automation maturity, and other quality attributes deserve some spotlight for course correcting if needed.

Separating time to ask “how is our technical debt doing?” ensures the team regularly inspects the accumulating interest payments from taking shortcuts. Reviews prevent getting too deep in denial about growing impediments swept under the rug during ongoing sprints.

Track Overall Technical Debt Metrics

Quantifying technical debt provides data-driven insights to inform backlog priorities. By tracking code quality over a baseline instead of vaguely talking about “too much technical debt”, teams obtain objective trends to target improvements and communicate needs.

Measuring debt might include legacy code ratios, antiquated dependencies counts, time spent on defects, test automation coverage growth, rates of code deprecation, metrics on code smells and complexity, component stability based on commit frequency, application security assessments, and counts of temporary quick fixes.

Addressing the “Invisible Work” Problem

Make All Work Transparent with a Definition of Done

Much of the invisible work in Scrum stems from unclear definitions of “done” for user stories. When teams inconsistently interpret expectations, technical tasks get overlooked such as infrastructure upgrades, testing harnesses, operational hooks, technical documentation, and other aid activities.

Making a consistent definition of done ensures software fully works as intended before calling a user story complete. This transparency identifies misaligned ideas of workflow completeness and avoids parties assuming others will handle backend aspects.

Timebox Technical Tasks

Scrum teams often disregard invisible work during sprint planning by leaving technical task durations open-ended. By not timeboxing engineering support activities the same as features, technical debt flows freely into the gaps between scattered priorities.

Putting duration bounds on infrastructure enhancements, bug fixing, test coverage, automation scripts, performance testing, security reviews, and tech-focused projects squeezes work into the sprint like any other user story. This forces reality checks on estimate accuracy.

Rotate Technical Ownership Across the Team

Allowing a subset of the team to always handle technical tasks makes that work invisible to others. By only having one or two resident experts responsible for infrastructure and architecture there is no oversight to identify growing impediments.

Broadening technical responsibility across all team members through paired programming, cross-training, and rotating secondary skillsets spreads awareness of technical debt challenges. Varying expertise through a shared codebase encourages proper encapsulations and simpler interfaces to enable wider implementation familiarity.

Conclusion: Technical Excellence Enables Agility

Neglecting the accumulation of technical debt for too long impacts product quality, customer satisfaction, developer happiness, and business throughput. Prioritizing technical excellence is not opposed to agility outcomes; it enables greater agility over time.

By transparently addressing technical debt with every iteration, implementing appropriate testing automation, modular design principles, clear coding standards, and shared ownership of system health, Scrum teams can reliably deliver more innovations faster. Sustainable speed comes from balanced focus between short-term feature needs and maintaining long-term integrity of the product.

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