Addressing Recurring Early Sprint Completion: Improving Estimation And Planning

The Problem of Repeatedly Finishing Sprints Early

Agile software development teams working in sprints often face the issue of repeatedly completing user stories and finishing sprints earlier than planned. This consistent pattern points to systemic problems in the sprint planning and estimation process.

When sprints continually finish ahead of schedule, it leads to frustration for both team members and stakeholders. The development team capacity is not being fully leveraged, leading to perceptions that the team is underutilized or inefficient. Early sprint completion also reduces team productivity and velocity over time.

The root cause behind consistent early sprint completion lies in ineffective upfront planning and effort estimation activities. Common contributors include:

• Not decomposing larger user stories into detailed, testable tasks
• Failing to account for all aspects of work in task estimates
• Relying too much on historical velocities rather than task complexity

To maximize team productivity, better sprint estimation and planning practices are required in order to plan appropriately scoped sprints that finish closer to on time.

Better Understanding User Stories and Tasks

A core component behind inaccurate sprint estimation is poor decomposition of user stories into tasks during sprint planning. To properly estimate level of effort, the team needs to break down user stories into testable, executable tasks as part of backlog grooming.

Tasks should capture discrete units of work, and the full scope of effort required to complete a user story should be accounted for. This includes tasks for design, testing, documentation, infrastructure changes, and ancillary activities. Teams that do not invest time in task breakdown end up missing key components of effort in their estimates.

In addition, teams should analyze the complexity, risk, and unknowns associated with each task during planning poker. Surfacing technical spikes and unknown work early on improves estimate accuracy. Checklists can guide task decomposition to ensure consistent coverage of all work required.

Historical velocities should be balanced with a bottoms-up estimation of each task’s complexity. While past performance sets helpful benchmarks, teams should not solely extrapolate based on previous sprints. Factors like technical debt, infrastructure upgrades, and solution complexity change over time.

Improving Effort Estimation at the Task Level

During sprint planning, agile teams frequently rely on expert judgment techniques like planning poker to estimate level of effort for user stories. However, estimates at the user story level tend to be inaccurate. By focusing estimation activities at the task level, teams can improve precision.

Two recommended techniques for task-level estimation are wideband delphi and planning poker. In wideband delphi, team members first estimate independently, then discuss estimates as a group before re-estimating. This surfaces additional details and leads to consensus.

Planning poker is similar, but adds gamification elements. Each team member plays a card with their estimate, and differences in estimates are debated. Several rounds lead to alignment. Both methods lead to greater estimate accuracy.

When estimating at the task level, teams should explicitly call out non-development items and unknowns as distinct tasks. This prevents implicit assumptions that lead to underestimated effort. Time for testing, infrastructure, Technical Debt paydown, and other ancillary work must be planned for.

By combining improved task breakdown, expert-based estimation techniques, and explicitly accounting for uncertainties, teams produce higher fidelity effort estimates less prone to underestimation bias.

Tracking Progress and Adjusting In-Sprint

Even with rigorous sprint planning and estimation, project unknowns and changing priorities lead to plan deviations. Agile teams must actively track progress against sprint goals, and make in-flight course corrections as needed.

Monitoring daily burn down gives insight into any estimation shortfalls or overruns. If significantly ahead of schedule, teams should pull in additional user stories after re-estimating level of effort. Doing so requires ongoing grooming of the backlog.

Conversely, underperforming user stories may need reevaluation and splitting into more granular tasks. Adding resources like pairing team members can help unblock stalled work. Teams should also assess components missing from the original estimates, and call out newly discovered unknowns or spikes.

Continuous stakeholder engagement enables teams to surface emerging needs in real-time. Product owners can rapidly prioritize newly identified user stories into the current sprint with team input on feasibility. This balances throughput with responding to business impacts.

Improving Estimation As a Continuous Process

Finally, teams should treat estimation capability as a continuous improvement activity, not a fixed competency. Dedicate time during retrospectives to review accuracy of task-level estimates from completed sprints.

Analyze patterns behind poor estimates, be it inadequate task breakdowns, cognitive biases like anchoring or overconfidence, or lack of accounting for testing/infrastructure activities. Compile lessons learned and best practices for estimating different work types.

Feed findings back into sprint planning sessions so future estimates incorporate new learnings. Over successive sprints, the team refines its ability to size tasks and plan appropriately scoped sprints.

By inspecting and adapting around estimation challenges, teams avoid systemic downward estimate biases that lead to early sprint completion release after release. Ongoing refinement of estimation approaches keeps accuracy high over time.