arXiv subjects

Qingshuang Bao

Publications and source records attributed to Qingshuang Bao.

2 recordsLinked to original sources

arXiv2026

Rethinking Technology Stack Selection with AI Coding Proficiency

Large language models (LLMs) are now an integral part of software development workflows and are reshaping the whole process. However, existing technology selection methods mainly focus on the inherent attributes of technologies, overlooking whether the LLM can effectively leverage the chosen technology. Therefore, teams using LLM assistants risk choosing technologies that cannot be used effectively by LLMs, yielding high debugging effort and mounting technical debt. We foresee a practical question in the LLM era, is a technology ready for AI-assisted development? In this paper, we first propose the concept, AI coding proficiency, the degree to which LLMs can utilize a given technology to generate high-quality code snippets. We then conduct the first large-scale empirical study examining AI coding proficiency across 170 third-party libraries and six LLMs. Our findings reveal that libraries with similar functionalities can exhibit up to 84% differences in the quality score of LLM-generated code. These gaps can be translated into real engineering costs and steer developer choices toward a narrow set of technologies, threatening technological diversity in the ecosystem. We call on the community to integrate AI coding proficiency into technology selection frameworks and develop mitigation strategies, preserving competitive balance in AI-driven development.

cs.SE↗

arXiv2025

The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation

Large Language Models (LLMs) have emerged as the new recommendation engines, surpassing traditional methods in both capability and scope, particularly in code generation. In this paper, we reveal a novel provider bias in LLMs: without explicit directives, these models show systematic preferences for services from specific providers in their recommendations (e.g., favoring Google Cloud over Microsoft Azure). To systematically investigate this bias, we develop an automated pipeline to construct the dataset, incorporating 6 distinct coding task categories and 30 real-world application scenarios. Leveraging this dataset, we conduct the first comprehensive empirical study of provider bias in LLM code generation across seven state-of-the-art LLMs, utilizing approximately 500 million tokens (equivalent to $5,000+ in computational costs). Our findings reveal that LLMs exhibit significant provider preferences, predominantly favoring services from Google and Amazon, and can autonomously modify input code to incorporate their preferred providers without users' requests. Such a bias holds far-reaching implications for market dynamics and societal equilibrium, potentially contributing to digital monopolies. It may also deceive users and violate their expectations, leading to various consequences. We call on the academic community to recognize this emerging issue and develop effective evaluation and mitigation methods to uphold AI security and fairness.

cs.SE↗