Runtime efficiency is a critical factor that impacts both software quality and user satisfaction. There are many approaches proposed for code optimization to improve runtime efficiency. Traditional code optimization methods operate on intermediate representations (IRs) during compilation for static languages. They are effective but struggle to handle dynamic languages that do not require compilation. Recently, large language models (LLMs) have been leveraged to directly optimize source code in dynamic languages. However, these methods fail to identify suitable optimization targets and usually conduct incomprehensive single-level optimization. To address these challenges, we propose Optimo, a multi-level LLM-based code optimization approach built on a novel Mixture-of-Prompts (MoP) architecture. In the MoP architecture, Optimo identifies time-critical code structures as performance bottlenecks via differential profiling. These structures are then routed to some optimization strategies, akin to expert models in MoE, each tailored to optimize specific code patterns. Unlike traditional approaches that focus only on statement-level optimizations, Optimo operates at four levels of abstraction, ranging from coarse-grained algorithmic improvements to fine-grained optimizations in API usage. We evaluate Optimo on two code efficiency benchmarks, COFFE and Effibench. Our results demonstrate that Optimo achieves an up to 57.48% opt%, i.e., the percentage of optimized programs that are correct and at least 10% faster than the original programs, and an up to 3.97x speedup when optimizing human-written code, and it consistently outperforms the best baseline by up to 96.51% in terms of opt%. Furthermore, Optimo achieves an up to 42.42% opt% and an up to 13.51x speedup when optimizing LLM-generated code.
Large Language Models (LLMs) have advanced code completion, but their ability to generate API usages compatible with evolving third-party libraries (TPLs) remains uncertain. As TPL APIs frequently change, LLMs risk producing code incompatible with installed library versions, causing build failures or incorrect behaviors. We define such issues as Incompatible Third-party Library API Usage (ITAU) and conduct a systematic study to evaluate how state-of-the-art LLMs handle this challenge. To this end, we propose an automated framework that builds a versioned TPL API Knowledge Base and a large-scale benchmark with 10,867 realistic code completion tasks. Through comprehensive evaluation of six state-of-the-art LLMs, we find that even top-performing models frequently generate incompatible completions. We further propose two lightweight solutions, Real-time Detection and Lightweight Repair, to mitigate ITAUs. This framework and benchmark provide a foundation for more compatibility-aware code generation in evolving software ecosystems.
Li Lin, Yaorui Fei, Yunfeng Shen et al.· ACM Transactions on Software...· 0 citations
Code refinement, a process of revising flawed code changes based on the code review comments, is a fundamental practice in software development for ensuring software quality and maintainability. Recent approaches using large language models (LLMs) show promise in automating this process, yet they typically rely only on review comments and a single code snippet, neglecting broader repository context. When a code refinement task requires invoking reviewer-unspecified identifiers (e.g., variables, functions, or classes) that are defined elsewhere in the repository, the LLM must accurately predict the exact identifier signature and usage, otherwise it could easily result in fatal errors. Prior work demonstrates that 90.59% of such knowledge-conflicting hallucinations cause all test cases to fail. Notably, over 60% of real-world code refinement scenarios in most popular Github repositories depend on repository-level knowledge, yet none of the existing code refinement works leverage this information. Moreover, current datasets do not distinguish between context-dependent and context-independent instances, contain many low-quality instances, and rely on lexical metrics (e.g., BLEU) that poorly correlate with human judgment. These limitations hinder reliable evaluation of code refinement tools in real-world, context-dependent scenarios. To address this gap, we introduce ReCoRe-Bench, a high-quality benchmark of 1,000 code refinement instances from 328 Python, Java, and JavaScript repositories that focused on one of the most challenging code refinement scenarios that strictly requires repository-level knowledge reasoning. To ensure repository-level context is required and to achieve strong review-revision alignment, we applied a strict identifier-based heuristic filtering pipeline and manually filtered low quality instances containing vague comments or unrelated review comments. To better evaluate whether an LLM correctly retrieves and resolves identifiers defined in the repository, instead of inferring them from limited local context, we propose a new evaluation metric, ReDIs Match. Additionally, to address the weaknesses of current LLM-powered code refinement methods identified by ReCoRe-Bench, we propose a straightforward method, RepoRefiner, which retrieves repository-level context by collecting the full file content, extracting definitions of its identifiers, and summarizing these contexts to support code refinement. The experiment results show that the RepoRefiner outperforms current code refinement baselines, and the repository-level context is essential for the reliable code refinement. Our findings underscore both the promise and current limitations of LLMs in repository-level code refinement scenarios, highlighting the need for more effective static analysis techniques and prompt engineering for broader repository-level context exploration.
Ke Wang, Peng Lan, Jiakun Liu et al.· ACM Transactions on Software...· 1 citation