Does your technical debt backlog look endless? Are you thinking about pausing feature development to resolve tech-debt? Stop. What if I told you that a good chunk of your backlog can simply wait? Tech-Debt can seem overwhelming when we look at it as a loosely organised list. This can lead to several anti-patterns in how we deal with them. Attend this session where I will be sharing strategies we have been leveraging to identify high priority tech-debt items to make sure we are able to continue feature development while improving code health.

Problem Statement: Tech-Debt often accumulates until productivity takes a serious hit and then as a knee jerk reaction we try to clean it up all at once. At this point there is just one large list of issues with a loose sense of priority. Net net it gives an impression that we have a huge backlog. This can lead to several anti-patterns.

• Chicken and Egg Problem - Too much Tech-Debt, so feature development is slow. Since feature development is slow, we cannot set aside time to fix issues.
• Fixing the wrong issues - In the larger scheme of things, it may be counter-productive to fix low priority issues just because they are easy.
• Pausing Feature Development - Approaches such as "Technical Debt Sprints" where we pause features to resolve tech-debt are not be sustainable even if they offer some short term benefits.
• Local Optima - Patchy cleanups which lead to uneven code health across the code base.
• And many more

Solution: Understand the impact of each Technical Debt at Block, Category and Item level to narrow your backlog to those issues which matter. While there are several tools that help us identify tech-debt it is up to us to map our context on those issues. Attend this talk where I will be going over how to VISUALISE, TRIAGE, PRIORITISE and STRATEGISE in order to get a realistic view on your tech-debt. Also I will be sharing my experience about how we have been leveraging time boxes and capacity constraints as a tool to make sure we are only working on the most important tech-debt issues.

Topics that will be covered:

• Tech-debt resolutions strategies - Anti-Patterns
• Tech Debt - Understanding Size vs Impact
• Tech-Debt Manifestations - Matrix view of areas of code and types of problems.
• Visualise - Triage - Prioritise - Strategise
  • Visual techniques to understand your tech-debt backlog with code analysis tools - Examples with popular tools
    • Bubble Charts - Coverage vs LOC, Maintainability vs LOC etc.
    • Git History - Multiple ways of looking at changes to a piece of code
    • Active Code Paths - Mapping usage to issues
    • Mapping Project Management Data - Bugs, Stories that touch a piece of code
  • Triaging the backlog to quickly eliminate tasks that can wait
    • Block level - Leveraging Logical Architecture
    • Category vice - Example: Front-End, Backend, API etc.
    • Item vice elimination
    • Refactor vs Rewrite
  • Prioritising tech-debt with layers of detail such as - Churn, LOC, Coverage, Bugs etc. Hotspot Identification.
  • Strategise - Approach to resolving each tech debt item based on Tech-Debt Manifestation Matrix
• Tech-Debt resolution - Hypothesis based, Data Driven approach
  • A template to capture your hypotheses, experiments and learnings
  • Visual confirmation that the issue is resolved
  • Just-Enough resolution - The uncomfortably short time-box - Imposing constraints to avoid runaway clean-ups
  • Guard rails to avoid a repeat of the same issue
• Incorporating tech-debt resolution into your Iterations, Weeks, Sprints, etc.
  • Identifying the right cadence on how often you fix debt - Hours per Day, Days per Week etc.
  • Tech-Debt back log Grooming
  • Cycling through categories of tech-debt
• Measuring Progress
  • Short-term - Measuring immediate impact on the code
  • Medium-term - Productivity improvements (Readability, Issue resolution time, etc)
  • Long-term - Team Health - Knowledge Silos, New Team Member Onboarding Time