AI Coding Proficiency in Software Engineering

• AI coding proficiency is defined as the degree to which LLMs generate syntactically valid, functionally correct, maintainable, and robust code across various libraries.
• Empirical assessments reveal significant variability among models and libraries, highlighting differences up to 84% in output quality and indicating model-specific performance strengths.
• Integrating AI coding proficiency into technology selection can lower debugging costs and technical debt while mitigating risks associated with software ecosystem monoculture.

AI coding proficiency refers to the degree to which artificial intelligence—especially LLMs—can accurately, effectively, and efficiently generate, understand, and manipulate computer code in response to human inputs. This concept is increasingly central to both the deployment of generative AI in software development and the selection or benchmarking of software technologies in the era of LLM-assisted engineering. Recent literature proposes AI coding proficiency as a multidimensional, quantifiable property of tools, models, and workflows, impacting engineering outcomes, technology stack selection, and ecosystem dynamics (Zhang et al., 14 Sep 2025). This article synthesizes current research findings on how AI coding proficiency manifests, is measured, its consequences for development practices, and its implications for software diversity and risk.

1. Conceptualization and Formal Definition

The property of AI coding proficiency is explicitly introduced as the degree to which LLMs can utilize a given software technology—such as a third-party library—to generate syntactically valid, functionally correct, maintainable, and robust code (Zhang et al., 14 Sep 2025). Unlike traditional metrics (e.g., usability, documentation quality), AI coding proficiency is model- and scenario-dependent, reflecting how well an LLM “knows” and can operationalize the APIs or abstractions of a technology.

This proficiency is rigorously measured via a pipeline that generates code snippets in response to templated prompts, with the AI coding proficiency score for a model $m$ on library $l$ in scenario $s$ defined as: $$\mathcal{P} = \frac{1}{|P_{l, s}|} \sum_{p \in P_{l, s}} S^m(p)$$ where $S^m(p)$ is a multifactorial code quality score for each prompt $p$. High values of $\mathcal{P}$ indicate that the model can reliably produce high-quality outputs across a range of tasks for that library.

2. Empirical Assessment of Proficiency Across Technologies and Models

A comprehensive empirical paper assessing 170 third-party Python libraries across 61 task scenarios with six prominent LLMs demonstrates large variability in AI coding proficiency (Zhang et al., 14 Sep 2025). Key empirical findings include:

• Libraries with similar functional intent exhibit up to 84% differences in model-generated code quality.
• In 11.17% of competing library pairs, statistically significant differences in proficiency are found (evaluated via Cohen’s d).
• Certain libraries, regardless of their real-world popularity or human usability, elicit subpar outputs from multiple LLMs (e.g., deprecated API usage, syntax errors).
• Model-specific variation is substantial: in the dataset, Gemini 2.5 Flash exhibits the highest proficiency on 93 of 170 libraries, while others such as GPT-4o-mini or Claude Sonnet 4 only claim best-in-class on a handful.

Such findings highlight that "LLM-friendliness" is orthogonal to human-oriented design metrics and can meaningfully alter practical development costs, maintainability, and reliability.

3. Implications for Technology Stack Selection

The integration of AI coding proficiency into the technology selection workflow is transformative. When teams or organizations choose frameworks or libraries that demonstrate high AI coding proficiency, they benefit from higher rates of near-production-ready, error-minimal code, which in turn leads to significant reductions in debugging overhead and technical debt (Zhang et al., 14 Sep 2025). Conversely, conventional practices that consider only popularity or feature sets may result in the adoption of technologies for which LLMs generate poor or deprecated code, amplifying engineering risk and eroding velocity.

The paper further introduces the risk of a “Matthew effect”: as workflow automation with LLMs becomes widespread, technologies with high AI coding proficiency will be disproportionately adopted, potentially reducing ecosystem diversity. This tends toward a concentration around “LLM-friendly” libraries, with the attendant risks of monoculture, supply chain fragility, and competitive stagnation.

4. Failure Patterns and Mitigation Strategies

Repeated model errors with certain technologies are systematically categorized. Eight failure patterns are prominent in low-proficiency settings, such as:

• Incorrect functionality (failure to meet the core requirements of the prompt),
• Missing handling of edge cases,
• Use of deprecated APIs, and
• Security or performance issues introduced by incorrect code idioms.

The research identifies proven mitigation strategies:

Strategy	Impact on Proficiency
Few-shot prompting	Narrowed quality gaps between libraries, improved output consistency.
Regeneration with selection	Improved best-of-N code selection, reducing error rates.
Chain-of-thought prompting	Minimal improvement for code quality, more useful for explicit reasoning.

Community actions—such as supplying more high-quality, up-to-date usage examples in documentation, and model-side improvements to training coverage—are explicitly recommended. The paper advocates for AI proficiency assessments to be an integral component of technology selection frameworks.

5. Broader Impact on Engineering Workflows and Ecosystems

The consequences of AI coding proficiency extend beyond model evaluation:

• Engineering teams leveraging high-proficiency technologies realize lower integration/testing costs and increased productivity.
• The concept supports novel “Vibe Coding” workflows in which AI serves as a central collaborator rather than merely a code generator (Zhang et al., 14 Sep 2025).
• Over time, skewed adoption driven by proficiency scores can challenge technological diversity and foster monoculture risks; thus, balanced evaluation frameworks and possibly regulatory guidance are necessary to support a healthy, diverse software ecosystem.

Furthermore, model developers and library maintainers can act to improve AI coding proficiency, e.g., by augmenting model training data with high-quality, library-specific code samples and by designing APIs and documentation with LLM learnability in mind.

6. Future Research Directions

Potential avenues for advancing the paper and application of AI coding proficiency include:

• Extending benchmarks to multiple programming languages, frameworks, and complex architectural patterns.
• Developing composite technology selection frameworks that integrate AI coding proficiency with maintainability, performance, portability, and traditional criteria.
• Investigating advanced model adaptation or fine-tuning strategies that reliably elevate proficiency for underrepresented or emergent libraries.
• Pursuing diverse and representative training corpora to counteract creeping monoculture effects and preserve innovation.

In addition, there are strong calls for improved metrics that consider not only syntactic and functional correctness but also aspects like execution efficiency or cross-platform portability, further refining the concept of AI coding proficiency.

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AI coding proficiency is therefore a foundational construct for practitioners and researchers working at the intersection of LLMs and software engineering. Quantifying, understanding, and engineering for high AI coding proficiency in software technologies and workflows will be a determinative factor in the future of AI-assisted development and the shape of the software ecosystem (Zhang et al., 14 Sep 2025).