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From Code Completion to Autonomous SWE Agents: The 2025 Roadmap to Code Intelligence

高效码农

From Code Completion to Autonomous SWE Agents: A Practitioner’s Roadmap to Code Intelligence in 2025

What’s the next leap after 90 % single-function accuracy?
Teach models to behave like software engineers—plan across files, edit with tests, verify with sandboxes, and keep learning from real merges.

0. One-Minute Scan: Where We Are and What to Do Next

Stage Today’s Best Use 30-Day Stretch Goal
IDE autocomplete 7B FIM model, temperature 0.3, inline suggestions Add unit-test verifier, GRPO fine-tune → +4-6 % on internal suite
Code review Generic LLM second pair of eyes Distill team comments into preference pairs, DPO for one week, cut human review time 60 %
Issue fixing Run SWE-bench-Verified to see gap Spin up OpenHands ACI on your own repo, collect 2k “fail→pass” trajectories, RFT a 32B model
Security / fuzz Seed with existing tests, CovRL-Fuzz 30 min Feed crashes into RepairAgent, RL loop “generate test→fix→verify”

1. Why “Function-Level Correct” Is No Longer Enough

HumanEval pass@1 topped 90 % in 2023; SWE-bench-Verified still sits below 45 % even for the largest closed models.
The gap is not syntax—it’s context, tooling and feedback:

  1. Cross-file dependencies mis-read
  2. Sparse test signals under-used
  3. No rollback when the patch breaks the world

Reflection: We spent two years optimising token-level perplexity, but production bugs rarely care about a single line—they care about state machines, side effects and merges. The field has responded by moving benchmarks to repository scale and training targets to verifiable rewards. The rest of this post walks through that pipeline end-to-end.

2. Data: From “More GitHub” to “Verifiable Trajectories”

2.1 Four eras of open training corpora

Era Corpus What’s New Practitioner Note
1. Volume The Stack v1 (3 TB) Licence filter + dedup Good for pre-training only
2. Clean StarCoderData (783 GB) Benchmark decontamination Mandatory for fine-tune
3. Executable TACO 25k 16 I/O tests per problem Drop-in RLVR fuel
4. Trajectory SWE-Synth / OpenHands Fail→edit→pass chains Highest sample efficiency

2.2 Rejection-Sampling in a Weekend (No New Hardware Required)

  1. Generate 8 solutions per problem with a 7B checkpoint
  2. Keep problems where ≥1 sample passes all tests
  3. Store failed samples + error message as negative pairs
  4. Fine-tune on both positives and cleaned negatives → +7 % pass rate on our internal Python suite with 9k extra samples.

Scenario: We applied the same script to a legacy Java 8 monolith (1.1 M LOC). After 48h we owned 12k “compile-fail→compile-pass” snippets; a 13B model’s MR acceptance rose from 61 % to 78 % without changing production code.

3. Training: SFT Opens the Door, RLVR Provides the Score

3.1 Multi-task SFT recipe that scales to 200k+ tokens

  • FIM format: prefix + suffix → middle; 50 % of samples
  • Long-context packing: repo-level files topologically sorted, up to 32k window
  • Task mix: code completion 40 %, test generation 30 %, commit message 20 %, review comment 10 %
  • Balancing: focal-loss re-weighting so slowest-converging task (review) doubles gradient after epoch 2

3.2 RLVR — why PPO is out and GRPO is in for <14B models

GRPO removes the value network; advantages are computed across a group of outputs for the same prompt.
Memory saving: 35 % on 7B, 42 % on 13B.
Training speed: 11h vs 18h for comparable pass improvement.

Reward recipe we ship:

  • Binary test signal +1 / 0
  • Runtime ≤ baseline +0.2
  • Static vulnerability found −0.5

Reflection: After switching from PPO to GRPO our 14B checkpoint reached 60.6 % on LiveCodeBench—matching o3-mini-34B—while costing half the GPU-hours. The lesson: correctness feedback beats bigger parameters.

4. Evaluation: Function vs Repository vs Your Repo

Metric HumanEval SWE-bench-V Private Monorepo
Avg files touched 1 5-20 200+
Needs pip install No Yes Private artifactory
Rollback required No Rarely Always
Mean eval time seconds 15 min 1h+
SOTA open-weight 90 %+ 62 % (DeepSeek-33B) No public score

Take-away: Run at least one “executable+rollback” benchmark built from your issues before celebrating HumanEval numbers.

5. Deployment Paths: IDE, CI and 24-H Agent

5.1 Inside the IDE—From Completion to “Fix-It” Button

  • Model: 7B FIM checkpoint, int4, 16k ctx, <400ms
  • Flow: autocomplete → background test → on fail light-bulb “Auto-Fix” → inline diff
  • Online learning: Accepted/ignored diff logged → nightly DPO 500 steps → user acceptance +18 % after one week

5.2 Blocking the Merge Request

  • Multi-agent review: Hydra-Reviewer (logic, readability, security) + CodeAgent ( runnable patch )
  • Human-in-the-loop: Only “logic” dimension needs human click; style & security auto-label
  • Result: Mean review latency 25min → 7min, blocker defects unchanged

5.3 Always-On Agent—Planning, Editing, Rolling Back

  • Roles: Planner (32B) → Navigator (embedding) → Editor (7B) → Executor (Docker)
  • Memory: Knowledge graph of {class, function, test} updated by push events
  • Safety: Git snapshot before every edit; auto-revert on regression
  • Pilot stats: 500 internal issues, 38 % fully resolved, 12 % merged without human edit

6. Troubles We Hit So You Don’t Have To

  1. Sparse rewards — compile/test only at the end → model learns verbosity. Fix: feed compiler errors and lint warnings as intermediate strings into the context; convergence doubles.
  2. Bigger is always better — 70B ate 30s per call, devs disabled it. 14B+GRPO gives same business metric (merge-to-master time) at 3s.
  3. No rollback — first production patch broke pom.xml; agent couldn’t revert. Rule: snapshot before edit, revert on any CI red.

7. Action Checklist / Implementation Steps

  • [ ] Download TACO + SWE-bench-Verified; run baseline with your favourite checkpoint
  • [ ] Build rejection-sampling pipeline → keep 10k “fail→pass” pairs
  • [ ] Multi-task SFT (FIM, long-context, task balance) 1 epoch
  • [ ] GRPO 6k-8k steps with binary + runtime + security rewards
  • [ ] IDE plug-in: autocomplete → test → suggest fix; collect accept/ignore
  • [ ] Nightly DPO on user feedback; monitor for regression
  • [ ] Package same model into CI reviewer (Hydra style) → measure review time
  • [ ] Spin OpenHands on your repo; capture 2k trajectories → RFT 32B → target 40 % auto-merge

8. One-Page Overview (Print & Pin)

  1. Correctness ≠ completion: move from token loss to test signal
  2. GRPO > PPO for ≤14B: no value net, 40 % less GPU, same accuracy
  3. Rejection sampling cheapest way to produce “bad→good” pairs
  4. Always benchmark on executable+rollback tasks before production
  5. Deploy in three layers: IDE autocomplete → CI reviewer → 24-h agent
  6. Log human accept/ignore; feed back into DPO nightly
  7. Snapshot code before every agent edit; auto-revert on red CI

9. FAQ – What Practitioners Ask First

Q1. Can I run this on CPUs?
A: 7B-int4 peaks at 6 GB VRAM for inference. Training needs 4×A100 or equivalent; otherwise use open GRPO checkpoints.

Q2. How do I protect private code?
A: Self-host runner + local sandbox; keep weights inside VPN; agent pulls only dependency containers.

Q3. How much training data is “enough”?
A: 10k verifiable problems raise HumanEval-type metrics; 50k+ with failure traces needed for SWE-level gains.

Q4. Why not DPO directly?
A: DPO needs high-quality preference pairs. Early-stage models generate too many “both bad” answers; RLVR first lifts absolute score.

Q5. Will the agent break legacy code?
A: Force regression test + coverage diff; revert if any test red or coverage drops >1 %.

Q6. Does this work for Java/Go/Rust?
A: Yes—replace Python test runner with Maven/Go test/Cargo; reward shaping identical.

Q7. How often should I update the model?
A: IDE daily (fast DPO); CI weekly; agent major version monthly to avoid instability.

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