Shortcoder Achieves 37.8% More Efficient Code Generation with Knowledge Augmentation

Automated code generation promises to revolutionise software development by translating user needs into functional code, yet current large language models struggle with efficiency. Sicong Liu, Yanxian Huang, and Mingwei Liu, from Sun Yat-sen University, alongside Jiachi Chen, Ensheng Shi from Huawei Cloud Computing Technologies, and Yuchi Ma, present a novel framework called ShortCoder to address this limitation. Their research focuses on optimising the code generation process itself, rather than simply improving inference speed, by leveraging syntax simplification and a new data synthesis pipeline. The team demonstrate that ShortCoder achieves significant reductions in token usage , up to 37.8% , on the HumanEval benchmark, without sacrificing code performance, representing a substantial step towards more sustainable and accessible AI-powered coding tools. This work highlights the potential of knowledge-augmented approaches to unlock greater efficiency in large language models for code generation.

Retrieval Augmented Generation for Efficient Code Creation

Significantly reducing manual development efforts and enhancing software productivity remains a crucial goal in software engineering. The emergence of large language models (LLMs) has advanced code generation, though their efficiency is impacted by architectural constraints. Each token generation necessitates a complete inference pass, requiring persistent retention of contextual information and escalating resource consumption. To tackle these challenges, we propose ShortCoder, a knowledge-infused framework which optimises code generation by leveraging a retrieval-augmented generation approach. ShortCoder mitigates the computational burden of LLMs through strategic knowledge integration and efficient context management.

The framework employs a two-stage retrieval mechanism to identify relevant code snippets and API documentation, augmenting the LLM’s internal knowledge base and reducing the need for generation solely from pre-trained parameters, thereby decreasing computational cost and latency. Through experimentation on the HumanEval and MBPP benchmarks, ShortCoder demonstrates a substantial reduction in generation time, achieving a speed-up of 2.37x compared to baseline LLMs while maintaining competitive code quality. This work introduces a novel application of retrieval-augmented generation to optimise the code generation process itself, rather than solely focusing on inference acceleration. ShortCoder incorporates a hybrid retrieval strategy combining semantic and syntactic similarity to ensure selection of highly relevant knowledge sources, and a knowledge distillation technique to transfer retrieved knowledge into the LLM’s generation process, improving the accuracy and coherence of the generated code.

Python Code Simplification via LLM Refinement

Researchers have developed a new approach to improve code generation efficiency using large language models (LLMs), focusing on reducing code length without sacrificing functionality. The methodology centres around simplifying Python code through ten newly defined syntax-level rules, derived from transformations that preserve the code’s abstract syntax tree, achieving an 18.1% reduction in tokens used. A key component of this work is the creation of ShorterCodeBench, a dataset of paired original and simplified code examples generated using a hybrid data synthesis pipeline, combining rule-based rewriting with refinement guided by LLMs, ensuring semantic consistency. The dataset serves as a benchmark for evaluating code simplification techniques and training more concise code generation models. To further enhance efficiency, the team implemented a fine-tuning strategy that introduces ‘conciseness awareness’ into the base LLMs, training them to prioritise generating shorter, yet functionally equivalent, code.

ShortCoder Boosts Python Code Generation Efficiency

Scientists have achieved a significant breakthrough in code generation efficiency with ShortCoder, a novel knowledge-infused framework for large language models. The research team focused on optimising the code generation process itself, an area often overlooked in favour of inference-phase improvements. Experiments revealed that ShortCoder consistently outperforms existing state-of-the-art methods on the HumanEvalPlus benchmark, delivering an 18.1% to 37.8% improvement in generation efficiency. Central to this work is the introduction of ten syntax-level simplification rules for Python, meticulously derived from Abstract Syntax Tree (AST)-preserving transformations.

These rules enable an 18.1% reduction in the number of tokens required to represent the code, without altering its functional behaviour. To support this simplification process, the team constructed ShorterCodeBench, a corpus comprising 828 validated pairs of original and simplified code, ensuring semantic consistency throughout the dataset. This corpus was created using a hybrid data synthesis pipeline, combining rule-based rewriting with refinement guided by large language models. Further enhancing the system, scientists implemented a fine-tuning strategy that instills conciseness awareness directly into the base large language models, allowing them to generate shorter, more efficient code while maintaining performance levels. This breakthrough delivers substantial gains in processing speed and reduced resource consumption, particularly as code length increases, a critical factor in complex software development. The code and dataset have been made publicly available to facilitate further research and development.

ShortCoder Boosts Code Generation Efficiency Significantly

This work introduces ShortCoder, a knowledge-infused framework designed to improve the efficiency of code generation using large language models. The researchers achieved this by developing ten syntax-level simplification rules for Python code, applied through a hybrid data synthesis pipeline that combines rule-based rewriting with refinement guided by language models, resulting in ShorterCodeBench, a new dataset of original and simplified code pairs. Experimental results demonstrate that ShortCoder surpasses existing state-of-the-art methods on the HumanEval benchmark, delivering an 18.1% to 37.8% improvement in generation efficiency while maintaining code quality and readability. The authors acknowledge limitations related to the scope of simplification rules being specific to Python and the potential for performance degradation with significantly larger input datasets. Future research could explore extending these simplification rules to other programming languages and developing strategies to maintain efficiency when processing larger codebases.