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ARXIV:2604.01527 · AI CODING AGENTS · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01527AI CODING AGENTSSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALESmriti Jha · Matteo Paltenghi · Chandra Maddila · Vijayaraghavan Murali · Shubham Ugare · Satish Chandra · arXiv
A production-derived benchmark for evaluating AI coding agents, enabling more realistic performance assessment and driving improvements in agent capabilities.
Opportunity summary
Pain A production-derived benchmark for evaluating AI coding agents, enabling more realistic performance assessment and driving improvements in agent capabilities.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A production-derived benchmark for evaluating AI coding agents, enabling more realistic performance assessment and driving improvements in agent capabilities. This paper presents a methodology for curating production-derived benchmarks, illustrated through ProdCodeBench - a benchmark…
Benchmarks that reflect production workloads are better for evaluating AI coding agents in industrial settings, yet existing benchmarks differ from real usage in programming language distribution, prompt style and codebase structure. This paper presents…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our systematic analysis of four foundation models yields solve rates from 53.2% to 72.2% revealing that models making greater use of work validation tools,…
AI Coding Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A production-derived benchmark for evaluating AI coding agents, enabling more realistic performance assessment and driving improvements in agent capabilities.
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Paper Pack
10.48550/arXiv.2604.01527A production-derived benchmark for evaluating AI coding agents, enabling more realistic performance assessment and driving improvements in agent capabilities.
Abstract
Benchmarks that reflect production workloads are better for evaluating AI coding agents in industrial settings, yet existing benchmarks differ from real usage in programming language distribution, prompt style and codebase structure. This paper presents a methodology for curating production-derived benchmarks, illustrated through ProdCodeBench - a benchmark built from real sessions with a production AI coding assistant. We detail our data collection and curation practices including LLM-based task classification, test relevance validation, and multi-run stability checks which address challenges in constructing reliable evaluation signals from monorepo environments. Each curated sample consists of a verbatim prompt, a committed code change and fail-to-pass tests spanning seven programming languages. Our systematic analysis of four foundation models yields solve rates from 53.2% to 72.2% revealing that models making greater use of work validation tools, such as executing tests and invoking static analysis, achieve higher solve rates. This suggests that iterative verification helps achieve effective agent behavior and that exposing codebase-specific verification mechanisms may significantly improve the performance of externally trained agents operating in unfamiliar environments. We share our methodology and lessons learned to enable other organizations to construct similar production-derived benchmarks.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
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Viability
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Dimensions overall score 7.0
PROBLEM
A production-derived benchmark for evaluating AI coding agents, enabling more realistic performance assessment and driving improvements in agent capabilities. This paper presents a methodology for curating production-derived benchmarks, illustrated through ProdCodeBench - a benc...
METHOD
Benchmarks that reflect production workloads are better for evaluating AI coding agents in industrial settings, yet existing benchmarks differ from real usage in programming language distribution, prompt style and codebase structure. This paper presents a methodology for curatin...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our systematic analysis of four foundation models yields solve rates from 53.2% to 72.2% revealing that models making greater use of work validation tools, such as executing tests and invoking static anal...
WHY NOW
AI Coding Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
existing benchmarks differ from real usage in programming language distribution, prompt style and codebase structure.
Directly and explicitly stated in the abstract as the motivation for the work.
partial
ProdCodeBench - a benchmark built from real sessions with a production AI coding assistant. Each curated sample consists of a verbatim prompt, a committed code change and fail-to-pass tests spanning seven programming languages.
Explicitly and directly stated in the abstract as the core contribution of the paper.
partial
We detail our data collection and curation practices including LLM-based task classification, test relevance validation, and multi-run stability checks
Directly stated in the abstract as key components of the methodology.
partial
Our systematic analysis of four foundation models yields solve rates from 53.2% to 72.2%
Directly stated numeric result in the abstract.
partial
models making greater use of work validation tools, such as executing tests and invoking static analysis, achieve higher solve rates.
Directly stated as a finding from the analysis in the abstract.
partial
This suggests that iterative verification helps achieve effective agent behavior
Strongly implied as a conclusion from the result about validation tools, stated as a suggestion in the abstract.
partial
exposing codebase-specific verification mechanisms may significantly improve the performance of externally trained agents operating in unfamiliar environments.
Strongly implied as a conclusion from the result about validation tools, stated as a suggestion in the abstract.
partial
Benchmarks that reflect production workloads are better for evaluating AI coding agents in industrial settings
Directly stated as a premise in the opening sentence of the abstract.
partial
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Concepts
Methods
Materials
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A production-derived benchmark for evaluating AI coding agents, enabling more realistic performance assessment and driving improvements in agent capabilities.
Segment
AI Coding Agents
Adoption evidence
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Integration burden
missing
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No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
Build Passport does not name an implementer.
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Operator workflow not sourced.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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