Batch Processing Multiple Issues: A Practical Guide

Introduction

You have ten Issues in the backlog. Processing them one by one could take days.

One of DevLoop Runner's core strengths is the ability to process multiple Issues in parallel. Used correctly, parallel processing completes ten Issues in a fraction of the time sequential processing would take.

That said, "throw everything at it simultaneously" isn't the right approach. Parallel processing has nuances — the results depend on which Issues you select, how you order them, and how you handle the output.

This guide covers the practical techniques for batch-processing multiple Issues efficiently with DevLoop Runner.

Why Parallel Processing Works

Let's start with why parallel processing is more efficient.

Maximizing Throughput

If a single Dev Run takes 30 minutes, five sequential Issues take 2.5 hours. In parallel, all five run simultaneously, and the total time equals the longest single run plus a small overhead.

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Eliminating Idle Time

In sequential processing, you wait for a Dev Run to finish, review the PR, then move to the next Issue. Those waiting gaps add up.

With parallel processing, multiple Dev Runs progress simultaneously. While you review one PR, the next is already being prepared. You can review continuously without dead time.

Consistent Base

PRs generated in the same batch are based on the same codebase snapshot. Sequential processing with merges in between changes the base, increasing the likelihood of conflicts in later PRs. Parallel processing keeps each PR independent and predictable.

Which Issues Are Good for Batching?

Not every Issue belongs in a parallel batch. These characteristics indicate a good fit.

Mutual Independence

The most critical requirement: Issues must be independent. Issue A's outcome should not be a prerequisite for Issue B. They should not modify the same files.

Examples of independent Issues:

• UI improvements on different pages
• Refactoring different modules
• Adding different API endpoints
• Improving test coverage for separate modules

Appropriate Granularity

Too-small Issues create management overhead that outweighs parallel gains. Too-large Issues increase the risk of file conflicts.

Granularity guidelines:

• Changes scoped to a single component or module
• Roughly 5-10 files changed
• Completes in a single Dev Run within 15-45 minutes

Same Category of Improvement

Parallel processing is especially effective when applying the same type of improvement across multiple modules.

• Error handling improvements across all modules
• Accessibility updates for all components
• Validation additions to all API endpoints

Prioritization and Execution Order

Even with parallel processing, it's more efficient to organize Issues into batches and execute them in stages rather than running everything at once.

Dependency-Based Batching

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Batch 1 has no dependencies, so all Issues run in parallel. Once all Batch 1 PRs are merged, Batch 2 runs in parallel. Finally, Batch 3 executes.

Priority Criteria

Within each batch, prioritize Issues using these criteria:

Criterion	High Priority	Low Priority
Blocking	Blocks other Issues	Blocks nothing
Risk	Security, data integrity	Cosmetic, convenience
Impact scope	Affects multiple modules	Single module only
Urgency	User-facing bugs	Internal improvements

Pitfalls of Parallel Processing

Parallel processing requires awareness of several potential issues.

File Conflict Risk

When parallel Dev Runs modify the same file, merge conflicts arise at merge time.

Mitigation strategies:

• Don't place Issues that likely touch the same files in the same batch
• Run Issues that modify shared config files (package.json, tsconfig.json) independently
• Run Issues that change shared components or utilities first, merge them, then run dependent Issues

Base Branch Management

All parallel PRs branch from the same base (usually main). Merging one PR makes the others' base stale.

How to handle it:

• Small, non-overlapping changes typically merge without issues
• Large changes or PRs touching the same areas may need rebasing after merge
• The safest approach: merge one batch at a time, then run the next batch from the updated main

Integration Verification

After merging individually reviewed PRs, verify that the system works correctly as a whole. Each PR may be correct in isolation, but combinations can introduce unexpected issues.

Efficient Review Strategies

When multiple PRs land simultaneously, review efficiency becomes critical.

Triage First

Start by scanning all PRs and sorting them into categories:

• Ready to merge: Small changes, no issues
• Needs careful review: Large changes or design decisions required
• Needs revision: Problems identified

Process Small PRs First

Reviewing and merging smaller PRs first clears the queue quickly. Save larger PRs for focused review time.

Leverage PR Descriptions

DevLoop Runner generates PR descriptions that explain the change summary and design intent. Read the description before diving into the diff — it significantly speeds up comprehension.

Batch Your Reviews

Batching reviews is more efficient than reviewing each PR the moment it completes. Wait until 3-5 PRs are ready, then review them in a block. This reduces context switching between review and other tasks.

Choosing Execution Modes

DevLoop Runner offers multiple execution modes, and batch processing benefits from mixing them strategically.

Full Phase Execution

Runs all phases: planning, requirements, design, test scenarios, implementation, test implementation, test execution, documentation, results, and project evaluation.

Best for:

• New feature additions
• Changes requiring design decisions
• Issues that need tests and documentation alongside implementation

Plan Only

Executes only the planning phase to produce a design outline.

Best for:

• Batch-planning multiple Issues to review the overall approach before committing to implementation
• Issues with ambiguous scope that need clarification before building

Implementation Only

Skips planning and jumps straight to implementation.

Best for:

• Issues with crystal-clear requirements (typo fixes, dependency updates)
• High-volume minor improvements

Combining Modes in Batch Processing

For maximum efficiency, match execution modes to Issue characteristics.

Batch 1 (Planning phase):
  - Run Issues #1-#5 in Plan Only mode
  - Review the plans

Batch 2 (Implementation phase):
  - Run reviewed Issues in Full or Implementation Only mode
  - Use Implementation Only for minor fixes to speed things up

Batch 3 (Finalization):
  - Review and merge PRs
  - Run Finalize

Summary

• Parallel processing maximizes throughput and eliminates idle waiting time
• Independent Issues with appropriate granularity are the best candidates for batching
• Organize batches by dependency and execute them in stages
• Watch out for file conflicts and stale base branches
• Streamline reviews with triage, small-PR-first processing, and PR description leverage
• Match execution modes to Issue characteristics for additional efficiency gains

DevLoop Runner's parallel processing isn't just about speed. The process of organizing Issues, setting priorities, and designing batches forces you to rethink your entire development workflow. When facing ten Issues, the question shifts from "how do I process these?" to "how do I structure these?" That shift in perspective is where the real productivity gains begin.