Features

As a user, I want to create and save a custom view from my selected filters, so that I can easily return to it later and the system can use the context to personalize recommendations or train the agent for me. Acceptance Criteria Trigger Creation Pop-up When I click "Save View" after applying filters, a pop-up appears. Pop-up Fields Name of View (required): text field to give the view a recognizable name. Context (optional): a free-text field where I can describe the purpose of the view (used to train the agent later for personalized insights). Editing After creation, I can edit both the Name and Context of the saved view. Saving & Access Saved views appear in my list of views and can be selected/applied directly. Persistence Saved views are tied to my user account so I can access them on subsequent sessions.

Feature Request: Add Pick Up Time in Review Collaboration Summary Introduce a unified Pick Up Time metric that captures the time from PR submission to the first reviewer interaction (comment, approval, or rejection). This will replace the current split metrics (“First Comment” and “Time to Approval”) as the primary measure of the initial touchpoint. Problem / Motivation Today, responsiveness in review collaboration is measured in two ways: First Comment Time → time until a reviewer leaves a comment. Time to Approval → time until a reviewer approves the PR. However, this split doesn’t fully reflect the first touchpoint between submitter and reviewer, since the reviewer’s first action could be either a comment, an approval, or a rejection. Teams lack a holistic view of when a PR is actually “picked up” for the first time. Proposed Solution Introduce a Pick Up Time metric defined as the time from PR submission to the first reviewer action of any type (comment, approval, or rejection). Display Pick Up Time alongside existing review metrics in dashboards. Maintain the ability to drill down into “First Comment Time” and “Time to Approval” separately for more detailed analysis. Expected Benefits Provides a single, clear indicator of review responsiveness. Reduces ambiguity about what counts as the “first response.” Helps identify bottlenecks earlier in the review workflow. Complements existing metrics by giving both a holistic view (Pick Up Time) and detailed views (comment vs approval).

Currently, DORA metrics (e.g., Lead Time for Changes, Deployment Frequency) are calculated primarily from deploy branches. However, some customers want visibility across the entire development lifecycle, including feature, hotfix, and support branches. We’re extending support to multiple branch types so teams can track the full development flow — not just production deployments. Key Enhancements: Track Activity Across All Branches: Includes feature branches, release branches, bugfix/hotfix branches, and long-running dev branches. Full-Cycle DORA-like Metrics: Get insights into Lead Time, Change Volume, and Activity Density across the whole Git workflow — not just deploys. Custom Branch Filters: Teams can define which branches to include or exclude based on naming conventions or labels (e.g., feature/ , bugfix/ ). Why it Matters: Offers a complete view of the development process, especially for teams not deploying daily. Improves understanding of effort spent pre-deployment (e.g., reviews, merges, testing). Helps detect bottlenecks in feature development, not just in release cycles. User Value: This update enables more accurate benchmarking and holistic process analysis — aligning better with modern branching strategies used in GitFlow and trunk-based development alike.

Some organisations need to calculate costs or other KPIs using custom attributes (e.g., customer segmentation, labels, or priority levels). To support this, we are introducing Custom Fields Mapping: Create & Map Custom Fields Users will be able to define their own custom fields in Leanmote and map them directly to existing custom fields in Jira (first supported tool, with others to follow). Example: Map “Labels”, “Customers”, or “Priority” in Jira to custom fields in Leanmote for richer analysis. Enhanced Filtering & Analysis Once mapped, these fields will automatically appear in filters and reports, enabling deeper analysis and more tailored KPIs. Example: Filter delivery metrics by customer, compare performance by priority level, or analyze outcomes across different labels. Phased Rollout Phase 1 (Early Access): Configuration available via Customer Success team for onboarding and setup. Phase 2 (Self-Service): Full management through our Admin Console, giving customers direct control over creating, editing, and mapping their fields.

I need to compare estimated hours or story points to actual completion at the strategic level

We’re introducing a new Flow Efficiency breakdown by issue type (e.g., Task, Bug, Story, Improvement) to help teams understand where their time and effort are being invested — and whether technical debt is consuming too much capacity. Key Capabilities: Efficiency by Issue Type: Analyze flow efficiency separately for Bugs, Tasks, Stories, and other custom issue types. Technical Debt Awareness: Identify trends such as excessive time spent on bugs or rework, highlighting technical debt accumulation over time. Effort Investment Insights: Visualize how engineering time is split across planned work vs. unplanned fixes or support tasks. Use Cases: Detect if reactive work (bugs, support) is slowing down feature delivery Justify refactoring or tech debt reduction efforts Improve planning balance between innovation and maintenance User Value: Empowers engineering leaders to make data-driven decisions about resource allocation, tech debt prioritization, and workflow stability.

Some users need to understand the complete cycle from inception

As a user I want to manage my projects integration with Leanmote

I want to understand performance delivery of specific sub-teams (one or more users)