Agent Configuration SWE-bench Runner
Repository Context Budget
64,000 tokens
16k
96k
192k tokens
Self-Healing Iterations (Max Turns)
4 Iterations
1 (Single-Shot)
4 (Standard)
8 (Deep Repair)
Candidate Samples (k for Pass@k)
k = 5 samples
k=1
k=5
k=10
Projected Pass@k Rate
88.4%
k=5 with self-healing
Run Cost (API & Sandboxing)
$0.42
128k input + 6.2k output
Sandbox Provisioning Latency
5.2 ms
Hardware KVM Isolation
Total Wall-Clock Runtime
18.4 s
3 LLM turns + 2 test executions
SWE-Bench Execution Harness Lifecycle
RESOLVED • ALL TESTS PASS
STAGE 01
AST Ingestion
0.8s • Tree-Sitter
STAGE 02
Patch Synthesis
6.2s • Extended CoT
STAGE 03
MicroVM Sandbox
4.1s • Pytest Harness
STAGE 04
Self-Healing Repair
7.3s • 1 Feedback Loop
--- a/django/core/handlers/base.py
+++ b/django/core/handlers/base.py
@@ -124,7 +124,9 @@ class BaseHandler:
for middleware_method in self._view_middleware:
- response = middleware_method(request, callback, callback_args, callback_kwargs)
+ if asyncio.iscoroutinefunction(middleware_method):
+ response = await middleware_method(request, callback, callback_args, callback_kwargs)
+ else:
+ response = middleware_method(request, callback, callback_args, callback_kwargs)
if response:
break
| Reasoning Engine | SWE-bench Verified (Pass@1) | Pass@5 (Self-Healing) | Avg Turns to Resolve | Cost Per Solved Issue |
|---|---|---|---|---|
| Claude 3.7 Sonnet | 70.3% | 88.4% | 2.8 turns | $0.58 |
| OpenAI o1 | 68.4% | 86.1% | 3.1 turns | $1.45 |
| OpenAI o3-mini (High) | 64.2% | 82.0% | 3.4 turns | $0.24 |
| DeepSeek-R1 | 62.8% | 80.5% | 3.6 turns | $0.18 |
University-Grade Engineering Guides
Autonomous Software Engineering Curriculum
Level 607 • Code Understanding
Repository Context Windows & AST Retrieval
Tree-sitter AST parsing, language server protocol (LSP) integration, call graph construction, and repo-level context packing.
Level 608 • Agentic Execution
Test-Driven Synthesis & Self-Healing Loops
Automated pytest harness interception, stack trace feedback injection, git diff patch verification, and convergence theory.
Level 609 • Systems Security
MicroVM Sandboxing: Firecracker & gVisor
Hardened multi-tenant execution: container escape defense, seccomp filters, Firecracker snapshotting, and ephemeral workspace teardown.
Playbook A13 • Enterprise DevOps
Enterprise CD with Autonomous Coding Agents
Production CI/CD integration: GitHub Actions agent runners, automated bug reproducers, human-in-the-loop merge gates, and SOC2 auditability.