Refactoring Benchmark Dashboard

graph_rag

runs: 5 • tasks: 5

Avg coverage 0.417

Pass rate 100.0%

Accept rate 100.0%

vector_only

runs: 5 • tasks: 5

Avg coverage 0.217

Pass rate 80.0%

Accept rate 80.0%

show rows with FAIL only

Task-level comparison

Columns: context coverage, pass (status==success), accept (pass & acceptance check ok), failure category

task	graph_rag				vector_only
	coverage	pass	accept	failure	coverage	pass	accept	failure
consolidate_base_score_computation 合并重复的 baseScore 计算逻辑，避免在多个地方重复计算，提升性能与一致性 query: LegacyScoringUtil.calculateBaseScore	0.667	OK	OK	success	0	OK	OK	success
introduce_region_context 修改 calculateBaseScore() 方法，支持 Region 上下文的传入，而不是硬编码参数 query: LegacyScoringUtil.calculateBaseScore	0	OK	OK	success	0	OK	OK	success
refactor_legacy_scoring_util 重构 LegacyScoringUtil 静态工具类，拆解成更加可扩展、解耦的模块，不要让 LegacyScoringUtil implements ScoringService 必须使用 LegacyScoringServiceAdapter implements ScoringService，并 delegate 到 legacy static query: LegacyScoringUtil.calculateBaseScore	0.5	OK	OK	success	0.5	FAIL	FAIL	retrieve_miss
remove_magic_numbers 去除硬编码的 magic number 和阈值，改为配置驱动的方式 query: LegacyScoringUtil.calculateBaseScore	0.667	OK	OK	success	0.333	OK	OK	success
split_campaign_decision_engine_responsibilities 重构 CampaignDecisionEngine，拆分出风控逻辑、推荐逻辑和策略装配，确保符合单一职责原则 query: CampaignDecisionEngine.decideOffer	0.25	OK	OK	success	0.25	OK	OK	success

Failure taxonomy • graph_rag

success — 5 (100.0%)

Failure taxonomy • vector_only

success — 4 (80.0%)

retrieve_miss — 1 (20.0%)

Case study: implicit dependencies captured by GraphRAG

Task: refactor_legacy_scoring_util — 重构 LegacyScoringUtil 静态工具类，拆解成更加可扩展、解耦的模块，不要让 LegacyScoringUtil implements ScoringService 必须使用 LegacyScoringServiceAdapter implements ScoringService，并 delegate 到 legacy static

Compare retrieved files in context_pack.json. GraphRAG can expand via call/dep edges and retrieve non-obvious neighbors that vector similarity may miss.

GraphRAG context

retrieved files: 5

src/main/java/com/icbc/marketing/DemoRunner.java
src/main/java/com/icbc/marketing/core/LegacyScoringUtil.java
src/main/java/com/icbc/marketing/service/CampaignDecisionEngine.java
src/main/java/com/icbc/marketing/strategy/AbstractBaseStrategy.java
src/main/java/com/icbc/marketing/strategy/impl/HighNetWorthStrategy.java

Vector-only context

retrieved files: 1

src/main/java/com/icbc/marketing/core/LegacyScoringUtil.java

Diff: only GraphRAG (4) / only Vector-only (0)

Only GraphRAG

src/main/java/com/icbc/marketing/DemoRunner.java
src/main/java/com/icbc/marketing/service/CampaignDecisionEngine.java
src/main/java/com/icbc/marketing/strategy/AbstractBaseStrategy.java
src/main/java/com/icbc/marketing/strategy/impl/HighNetWorthStrategy.java

Only Vector-only

(none)

Future work

Replace current success proxy with task-specific tests. Right now, “success” mostly means the project compiles / DemoRunner output stays the same. This can miss semantic regressions.
Use tests during agent iterations. When the agent performs repair across iterations, incorporate unit/integration test signals to guide edits and stop early.

Raw details

Expandable JSON for quick debugging / reproducibility.

consolidate_base_score_computation details (JSON)

{
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    "name": "consolidate_base_score_computation",
    "query": "LegacyScoringUtil.calculateBaseScore",
    "request": "需求：消除 baseScore 在引擎层与策略层的重复计算，保证一次决策只计算一次并复用同一口径。\n验收：输出不变；代码中不再出现同一决策链路的重复算分调用（可通过缓存到上下文/决策对象）。\n约束：复用方式应对未来扩展（如 Region 上下文）友好，避免再次散落计算逻辑。",
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  },
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          "target/classes/com/icbc/marketing/strategy/AbstractBaseStrategy.class",
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      }
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      "failure_cat": "success",
      "failure_detail": "",
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      }
    }
  }
}

introduce_region_context details (JSON)

{
  "task": {
    "name": "introduce_region_context",
    "query": "LegacyScoringUtil.calculateBaseScore",
    "request": "需求：为 base score 引入 Region 上下文，作为一等公民参与计算（支持默认 Region），而不是依赖 Map 随意塞 key。\n验收：不修改 DemoRunner 默认输入时输出保持不变；新增 Region 后可通过传入不同 Region 触发不同口径（可先预留接口/分支）。\n约束：保持向后兼容（原有调用仍可工作/可标记 deprecated），避免大范围破坏式改动。",
    "expected_files": [
      "src/main/java/com/icbc/marketing/core/ScoringService.java"
    ],
    "focus_node": "LegacyScoringUtil.calculateBaseScore",
    "focus_node_after": "ScoringService.calculateBaseScoreWithRegion",
    "description": "修改 calculateBaseScore() 方法，支持 Region 上下文的传入，而不是硬编码参数"
  },
  "runs": {
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      "coverage": 0.0,
      "failure_cat": "success",
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}

refactor_legacy_scoring_util details (JSON)

{
  "task": {
    "name": "refactor_legacy_scoring_util",
    "query": "LegacyScoringUtil.calculateBaseScore",
    "request": "需求：将 LegacyScoringUtil 的关键能力（算分/黑名单判断）从 static 工具类中解耦出来，形成可替换的服务/接口实现（允许内部先用适配器保留旧逻辑）。\n验收：DemoRunner 默认输入下输出保持不变；工程可编译运行。\n约束：引擎/策略层不应再与 LegacyScoringUtil 的 static 方法形成强耦合依赖。",
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          "src/main/java/com/icbc/marketing/strategy/AbstractBaseStrategy.java",
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      }
    },
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      "accept_success": false,
      "coverage": 0.5,
      "failure_cat": "retrieve_miss",
      "failure_detail": "failed_step=1",
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      "context_coverage": {
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remove_magic_numbers details (JSON)

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split_campaign_decision_engine_responsibilities details (JSON)

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    "query": "CampaignDecisionEngine.decideOffer",
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