taskgraph_ts/tasks/implementation/cost-benefit/dag-propagation.md at 131e3e929b50de5749c864fbd04c4e5e3ffda52d

Files

glm-5.1 131e3e929b Decompose architecture into 28 atomic implementation tasks

Break the @alkdev/taskgraph architecture specs into dependency-ordered
implementation tasks across 8 component directories: setup, schema,
error, graph, analysis, cost-benefit, frontmatter, api, and review.
Each task has clear acceptance criteria referencing specific architecture
docs. Three review tasks serve as quality gates at critical junction
points (schemas-and-errors, graph-complete, complete-library). The
dependency graph is validated acyclic with 9 topological levels enabling
significant parallelism across independent work streams.

2026-04-27 08:30:05 +00:00

2.4 KiB

Raw Blame History

id, name, status, depends_on, scope, risk, impact, level

name

status

depends_on

scope

risk

impact

level

cost-benefit/dag-propagation

Implement DAG-propagation effective probability computation

pending

cost-benefit/ev-calculation

graph/queries

schema/numeric-methods-and-defaults

moderate

high

phase

implementation

Description

Implement the DAG-propagation cost model in src/analysis/cost-benefit.ts. This is the core algorithmic contribution beyond the Rust CLI — it captures the structural reality that upstream failures multiply downstream damage.

Per cost-benefit.md, the algorithm:

Processes tasks in topological order
For each task with prerequisites, computes pEffective from intrinsic probability + upstream propagation
Upstream propagation: parentP + (1 - parentP) × qualityRetention for each parent
pEffective = intrinsic × product of all inherited quality factors

Acceptance Criteria

computeEffectiveP(taskId, graph, upstreamSuccessProbs, defaultQualityRetention, propagationMode) — internal helper
In dag-propagate mode: for each task in topological order:
- Get intrinsic probability from resolveDefaults(risk).successProbability
- For each prerequisite, compute inherited quality: parentP + (1 - parentP) × qualityRetention
- pEffective = intrinsic × product of all inherited quality factors
- Store task's actual success probability for downstream propagation (use pEffective if this is the task's real probability)
In independent mode: pEffective = pIntrinsic (no propagation)
Completed tasks (status: "completed"): propagate with p = 1.0 when includeCompleted: false
qualityRetention per edge defaults to 0.9, can be overridden per-edge via defaultQualityRetention option or edge attributes
Throws CircularDependencyError if graph is cyclic (needs topo sort)
Unit tests: simple chain (verify compounding effect), diamond graph, independent vs dag-propagate comparison matches Python research model results, completed task exclusion/propagation semantics

References

docs/architecture/cost-benefit.md — DAG-propagation algorithm, qualityRetention semantics
docs/architecture/decisions/004-workflow-cost-dag-propagation.md — ADR-004
docs/architecture/decisions/005-no-depth-escalation-v1.md — no depth escalation in v1

Notes

To be filled by implementation agent

Summary

To be filled on completion

2.4 KiB Raw Blame History Unescape Escape