feat(decision): implement influence DAG traversal in correction affected subtree computation #542

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opened 2026-03-04 01:00:26 +00:00 by freemo · 1 comment

freemo commented 2026-03-04 01:00:26 +00:00

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Field	Value
Type	Feature
Priority	Critical
MoSCoW	Must Have
Points	8
Milestone	v3.4.0
Assignee	freemo
Parent Epic	#394 (Epic: Decision Framework)
Depends On	None (existing code needs correction)

Background

The specification (§ Core Concepts > Decision Tree > Influence DAG, lines 18313-18341; § Correction > Affected Subtree, lines 28359-28391) explicitly states that the affected subtree for corrections must be computed by walking both the structural tree AND the influence DAG (decision_dependencies table), not just the structural tree.

Current bug: CorrectionService._compute_affected_subtree() at correction_service.py:447 only performs BFS on the structural tree (parent-child plan relationships). It does NOT traverse decision_dependencies edges. This means corrections that should cascade through influence relationships are silently missed.

The decision_dependencies table exists in the database schema but is not populated during decision creation, and not traversed during correction computation.

Acceptance Criteria

1. _compute_affected_subtree() traverses BOTH structural children AND decision_dependencies edges (union of both BFS traversals).
2. Decision creation code records influence relationships in decision_dependencies when a decision references or depends on outputs from other decisions.
3. Correction impact analysis correctly identifies all transitively-affected decisions through influence edges.
4. No infinite loops: cycle detection in influence DAG traversal (DAG should be acyclic, but defend against corruption).
5. Performance: traversal completes in O(V+E) where V = decisions, E = dependency edges.

Subtasks

1. Design

• Map the influence relationship types that should be recorded
• Design the extended BFS that unions structural + influence edges
• Document cycle detection approach

2. Implementation

• Update decision creation to populate decision_dependencies table
• Update _compute_affected_subtree() to traverse influence edges
• Add cycle detection guard
• Update correction cascade to handle influence-path affected nodes

3. Testing

• Unit test: structural-only subtree (existing behavior preserved)
• Unit test: influence-only subtree (new behavior)
• Unit test: combined structural + influence subtree
• Unit test: cycle detection (corrupt data handling)
• Integration test: end-to-end correction with influence cascade

4. Documentation

• Update CorrectionService docstrings
• Document influence relationship types

5. Integration

• Verify compatibility with existing correction workflows
• Verify decision_dependencies table schema is sufficient

6. Observability

• Log influence edge traversal counts
• Warn on cycle detection

7. Security

• Validate that influence traversal cannot be exploited to trigger unbounded cascades

Definition of Done

• All acceptance criteria met
• All subtask checkboxes checked
• Tests pass in CI
• Code reviewed and approved
• Existing correction tests still pass

## Metadata | Field | Value | |-------|-------| | **Type** | Feature | | **Priority** | Critical | | **MoSCoW** | Must Have | | **Points** | 8 | | **Milestone** | v3.4.0 | | **Assignee** | freemo | | **Parent Epic** | #394 (Epic: Decision Framework) | | **Depends On** | None (existing code needs correction) | ## Background The specification (§ Core Concepts > Decision Tree > Influence DAG, lines 18313-18341; § Correction > Affected Subtree, lines 28359-28391) explicitly states that the affected subtree for corrections must be computed by **walking both the structural tree AND the influence DAG** (`decision_dependencies` table), not just the structural tree. **Current bug:** `CorrectionService._compute_affected_subtree()` at `correction_service.py:447` only performs BFS on the **structural tree** (parent-child plan relationships). It does NOT traverse `decision_dependencies` edges. This means corrections that should cascade through influence relationships are silently missed. The `decision_dependencies` table exists in the database schema but is not populated during decision creation, and not traversed during correction computation. ## Acceptance Criteria 1. `_compute_affected_subtree()` traverses BOTH structural children AND `decision_dependencies` edges (union of both BFS traversals). 2. Decision creation code records influence relationships in `decision_dependencies` when a decision references or depends on outputs from other decisions. 3. Correction impact analysis correctly identifies all transitively-affected decisions through influence edges. 4. No infinite loops: cycle detection in influence DAG traversal (DAG should be acyclic, but defend against corruption). 5. Performance: traversal completes in O(V+E) where V = decisions, E = dependency edges. ## Subtasks ### 1. Design - [x] Map the influence relationship types that should be recorded - [x] Design the extended BFS that unions structural + influence edges - [x] Document cycle detection approach ### 2. Implementation - [x] Update decision creation to populate `decision_dependencies` table - [x] Update `_compute_affected_subtree()` to traverse influence edges - [x] Add cycle detection guard - [x] Update correction cascade to handle influence-path affected nodes ### 3. Testing - [x] Unit test: structural-only subtree (existing behavior preserved) - [x] Unit test: influence-only subtree (new behavior) - [x] Unit test: combined structural + influence subtree - [x] Unit test: cycle detection (corrupt data handling) - [x] Integration test: end-to-end correction with influence cascade ### 4. Documentation - [x] Update CorrectionService docstrings - [x] Document influence relationship types ### 5. Integration - [x] Verify compatibility with existing correction workflows - [x] Verify `decision_dependencies` table schema is sufficient ### 6. Observability - [x] Log influence edge traversal counts - [x] Warn on cycle detection ### 7. Security - [x] Validate that influence traversal cannot be exploited to trigger unbounded cascades ## Definition of Done - [x] All acceptance criteria met - [x] All subtask checkboxes checked - [x] Tests pass in CI - [ ] Code reviewed and approved - [x] Existing correction tests still pass

freemo added the

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Type

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labels 2026-03-04 01:01:14 +00:00

freemo added this to the v3.2.0 milestone 2026-03-04 01:01:15 +00:00

freemo referenced this issue 2026-03-04 01:02:08 +00:00

feat(correction): implement cross-plan correction cascading with child plan state handling #547

freemo modified the milestone from v3.2.0 to v3.4.0 2026-03-04 01:09:35 +00:00

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labels 2026-03-04 01:09:37 +00:00

freemo self-assigned this 2026-03-04 01:41:12 +00:00

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freemo referenced this issue from a commit 2026-03-04 04:22:08 +00:00

feat(decision): implement influence DAG traversal in correction affected subtree computation

freemo referenced this issue from a pull request that will close it, 2026-03-04 04:22:26 +00:00

feat(decision): implement influence DAG traversal in correction affected subtree computation #556

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labels 2026-03-04 04:23:27 +00:00

freemo commented 2026-03-04 04:23:53 +00:00

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Implementation Complete — PR #556

Design Decisions

1. Single BFS pass over union of structural + influence edges: Rather than performing separate BFS traversals and merging results, _compute_affected_subtree() now performs a single BFS that unions neighbors from both tree (structural) and influence_edges (DAG) adjacency lists at each node. This ensures O(V+E) complexity and consistent BFS visit order.

2. Cycle detection via visited set: The visited: set[str] prevents re-visiting nodes. When a node is encountered that was already visited, a correction.cycle_detected warning is logged (not an error, since cycles indicate data corruption, not a programming bug). BFS terminates cleanly regardless.

3. Backward-compatible API: All public methods (analyze_impact, execute_revert, execute_correction, generate_dry_run_report) accept an optional influence_edges: dict[str, list[str]] | None = None parameter. Existing callers that only pass decision_tree continue to work identically.

4. In-memory influence DAG store: DecisionService tracks influence edges in self._dependencies: dict[str, list[str]] (source → list[target]). record_decision() accepts dependency_decision_ids: list[str] | None and populates this store. get_influence_edges(plan_id) returns the plan-scoped adjacency list.

Key Code Locations (commit 7a7db52b)

• cleveragents.application.services.correction_service.CorrectionService._compute_affected_subtree — Extended BFS with influence DAG + cycle detection
• cleveragents.application.services.decision_service.DecisionService.record_decision — New dependency_decision_ids parameter
• cleveragents.application.services.decision_service.DecisionService._record_dependencies — Stores influence edges
• cleveragents.application.services.decision_service.DecisionService.get_influence_edges — Returns plan-scoped influence DAG adjacency list

Test Results

Suite	Result
Lint (ruff)	All checks passed
Typecheck (pyright strict)	0 errors, 0 warnings
Behave BDD	257 features, 8145 scenarios, 31446 steps — all passed
Robot Framework	1120 passed, 23 failed (all pre-existing) — 6/6 new tests passed
Coverage	97.0% (threshold: 97%)

New Test Scenarios

Behave BDD (features/influence_dag_traversal.feature): 11 scenarios covering structural-only, influence-only, combined, cycle detection, self-loop, deduplication, dependency recording, and revert execution.

Robot Framework (robot/influence_dag_traversal.robot): 6 integration tests including end-to-end correction with influence cascade.

ASV Benchmarks (benchmarks/influence_dag_bench.py): 4 benchmark suites measuring structural-only, influence-only, combined, and cycle detection performance at varying graph sizes.

Notes

• The in-memory _dependencies store is a cache/primary store following the same dual-mode pattern as the existing _decisions store. Future work could extend persistence to the decision_dependencies table via the UnitOfWork when DB-backed mode is active.
• The decision_dependencies SQLAlchemy model (DecisionDependencyModel) already exists with source_decision_id and target_decision_id columns — ready for persistence wiring.

## Implementation Complete — PR #556 ### Design Decisions 1. **Single BFS pass over union of structural + influence edges**: Rather than performing separate BFS traversals and merging results, `_compute_affected_subtree()` now performs a single BFS that unions neighbors from both `tree` (structural) and `influence_edges` (DAG) adjacency lists at each node. This ensures O(V+E) complexity and consistent BFS visit order. 2. **Cycle detection via visited set**: The `visited: set[str]` prevents re-visiting nodes. When a node is encountered that was already visited, a `correction.cycle_detected` warning is logged (not an error, since cycles indicate data corruption, not a programming bug). BFS terminates cleanly regardless. 3. **Backward-compatible API**: All public methods (`analyze_impact`, `execute_revert`, `execute_correction`, `generate_dry_run_report`) accept an optional `influence_edges: dict[str, list[str]] | None = None` parameter. Existing callers that only pass `decision_tree` continue to work identically. 4. **In-memory influence DAG store**: `DecisionService` tracks influence edges in `self._dependencies: dict[str, list[str]]` (source → list[target]). `record_decision()` accepts `dependency_decision_ids: list[str] | None` and populates this store. `get_influence_edges(plan_id)` returns the plan-scoped adjacency list. ### Key Code Locations (commit `7a7db52b`) - `cleveragents.application.services.correction_service.CorrectionService._compute_affected_subtree` — Extended BFS with influence DAG + cycle detection - `cleveragents.application.services.decision_service.DecisionService.record_decision` — New `dependency_decision_ids` parameter - `cleveragents.application.services.decision_service.DecisionService._record_dependencies` — Stores influence edges - `cleveragents.application.services.decision_service.DecisionService.get_influence_edges` — Returns plan-scoped influence DAG adjacency list ### Test Results | Suite | Result | |-------|--------| | Lint (ruff) | All checks passed | | Typecheck (pyright strict) | 0 errors, 0 warnings | | Behave BDD | 257 features, 8145 scenarios, 31446 steps — **all passed** | | Robot Framework | 1120 passed, 23 failed (all pre-existing) — **6/6 new tests passed** | | Coverage | **97.0%** (threshold: 97%) | ### New Test Scenarios **Behave BDD** (`features/influence_dag_traversal.feature`): 11 scenarios covering structural-only, influence-only, combined, cycle detection, self-loop, deduplication, dependency recording, and revert execution. **Robot Framework** (`robot/influence_dag_traversal.robot`): 6 integration tests including end-to-end correction with influence cascade. **ASV Benchmarks** (`benchmarks/influence_dag_bench.py`): 4 benchmark suites measuring structural-only, influence-only, combined, and cycle detection performance at varying graph sizes. ### Notes - The in-memory `_dependencies` store is a cache/primary store following the same dual-mode pattern as the existing `_decisions` store. Future work could extend persistence to the `decision_dependencies` table via the UnitOfWork when DB-backed mode is active. - The `decision_dependencies` SQLAlchemy model (`DecisionDependencyModel`) already exists with `source_decision_id` and `target_decision_id` columns — ready for persistence wiring.

freemo referenced this issue from a commit 2026-03-04 15:36:35 +00:00

feat(decision): implement influence DAG traversal in correction affected subtree computation

freemo closed this issue 2026-03-04 15:46:56 +00:00

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A bounty of $50 for any open-source contributor who provides a MR that solves this issue

  

Bounty

$500

A bounty of $500 for any open-source contributor who provides a MR that solves this issue

  

Bounty

$5000

A bounty of $5000 for any open-source contributor who provides a MR that solves this issue

  

Bounty

$750

A bounty of $750 for any open-source contributor who provides a MR that solves this issue

  

MoSCoW

Could have

Could have feature in order to satisfy the epic/legendary.

  

MoSCoW

Must have

Must have feature in order to satisfy the epic/legendary.

  

MoSCoW

Should have

Should have feature in order to satisfy the epic/legendary.

  

Needs Feedback

There are questions in the ticket that can not be completed until the project owner provides clarity.

  

Points

1

1 man-hours worth of work for an expert with no learning curve.

  

Points

13

13 man-hours worth of work for an expert with no learning curve.

  

Points

2

2 man-hours worth of work for an expert with no learning curve.

  

Points

21

21 man-hours worth of work for an expert with no learning curve.

  

Points

3

3 man-hours worth of work for an expert with no learning curve.

  

Points

34

34 man-hours worth of work for an expert with no learning curve.

  

Points

5

5 man-hours worth of work for an expert with no learning curve.

  

Points

55

55 man-hours worth of work for an expert with no learning curve.

  

Points

8

8 man-hours worth of work for an expert with no learning curve.

  

Points

88

88 man-hours worth of work for an expert with no learning curve.

  

Priority

Backlog

This ticket has backlogged priority and is not to be worked on yet

  

Priority

CI Blocker

Critical priority issue that blocks CI/CD pipeline and prevents PR merges

  

Priority

Critical

The priority is critical

  

Priority

High

The priority is high

  

Priority

Low

The priority is low

  

Priority

Medium

The priority is medium

  

Signed-off: Owner

When an epic or legendary is in review it must be signed off by owner, tech lead, and scrum master before being marked as completed.

  

Signed-off: Scrum Master

When an epic or legendary is in review it must be signed off by owner, tech lead, and scrum master before being marked as completed.

  

Signed-off: Tech Lead

When an epic or legendary is in review it must be signed off by owner, tech lead, and scrum master before being marked as completed.

  

Spike

A ticket for learning a tool or technology that is needed to be able to do future planning and design.

  

State

Completed

The ticket has been fully implemented, completed, and merged with the source code. This label should only be applied once a ticket is closed.

  

State

Duplicate

A ticket that represents the same content as an existing ticket.

  

State

In Progress

A ticket that is actively being developed.

  

State

In Review

A ticket that has had some code completed to implement but is waiting to pass peer review and is not yet merged in.

  

State

Paused

This ticket's work started but wasn't finished. It's on hold (likely in a feature branch) and will be resumed later, either due to a blocker or a delay.

  

State

Unverified

All new tickets start in this state. A developer may set it to show the ticket is unverified. This means we haven't agreed to work on it. It will either move to a verified state or be closed as wontdo.

  

State

Verified

The issue has been verified by a developer as legitimate. It will be worked on and verified tickets are now considered part of the backlog.

  

State

Wont Do

This ticket has been decided it wont be done. This may mean the bug has been determined to not be real (cant verify) or the feature is one we have decided we dont want to adopt.

  

Type

Automation

Any edits or discussion about the AI automated coding system.

  

Type

Bug

Something that doesnt work as intended.

  

Type

Discussion

Anytime a ticket represents a discussion about a subject and doesnt fall into one of the other categories.

  

Type

Documentation

An error or improvement needed in the documentation.

  

Type

Epic

Any first tier epic. That is, an epic which contains only issues as children and will not have sub-epics.

  

Type

Feature

Some new functionality not present.

  

Type

Legendary

A type of Epic which will contain other Epics.

  

Type

Refactor

A code change that restructures existing code without changing its external behavior.

  

Type

Support

Someone needs help using the project.

  

Type

Task

A generic task that doesnt fit into the other type categories.

  

Type

Testing

Work exclusively focusing on fixing or expanding testing.

No labels

auto/needs-reevaluation

controller-managed

auto/blocked-by-deps

auto/ci-timeout

auto/claimed-implementer

auto/claimed-merge

auto/claimed-reviewer

auto/driver-down

auto/invariant-violation

auto/last-attempt-tier-0

auto/last-attempt-tier-1

auto/last-attempt-tier-2

auto/last-attempt-tier-min

Automation Tracking

auto/needs-conflict-resolution

auto/needs-implementer

auto/postmortem

auto/ready-to-merge

auto/restart-throttled

auto/revert

auto/sentinel

auto/stale-inactivity

auto/unstable

Blocked

Bounty

$100

Bounty

$1000

Bounty

$10000

Bounty

$20

Bounty

$2000

Bounty

$250

Bounty

$50

Bounty

$500

Bounty

$5000

Bounty

$750

MoSCoW

Could have

MoSCoW

Must have

MoSCoW

Should have

Needs Feedback

Points

1

Points

13

Points

2

Points

21

Points

3

Points

34

Points

5

Points

55

Points

8

Points

88

Priority

Backlog

Priority

CI Blocker

Priority

Critical

Priority

High

Priority

Low

Priority

Medium

Signed-off: Owner

Signed-off: Scrum Master

Signed-off: Tech Lead

Spike

State

Completed

State

Duplicate

State

In Progress

State

In Review

State

Paused

State

Unverified

State

Verified

State

Wont Do

Type

Automation

Type

Bug

Type

Discussion

Type

Documentation

Type

Epic

Type

Feature

Type

Legendary

Type

Refactor

Type

Support

Type

Task

Type

Testing

Milestone

Clear milestone

No items

No milestone

v3.4.0

Projects

Clear projects

No items

No project

Assignees

Clear assignees

No assignees

freemo

1 participant

Notifications

Subscribe

Due date

The due date is invalid or out of range. Please use the format "yyyy-mm-dd".

No due date set.

Dependencies

No dependencies set.

Reference

cleveragents/cleveragents-core#542

No description provided.