Mingjie Zhou

Yixuan Li, Xinyi Liu, Weidong Yang et al.

Code retrieval aims to find relevant code snippets matching natural language queries within massive codebases, playing a vital role in software development. Recent advances leverage PLMs to bridge the semantic gap between natural language (NL) and programming languages (PL), significantly outperforming traditional information retrieval and early deep learning approaches. However, existing methods still face key challenges, including a fundamental semantic gap between human intent and machine execution logic, and limited robustness to diverse code styles. To address this, we propose PseudoBridge, a novel code retrieval framework that introduces pseudo-code as an intermediate, semi-structured modality to align NL semantics with PL logic. Specifically, PseudoBridge consists of two stages: First, we employ an LLM to synthesize pseudo-code, enabling explicit alignment between NL queries and pseudo-code. Second, we introduce a logic-invariant code style augmentation strategy, employing the LLM to generate stylistically diverse yet logically equivalent code implementations, and then align these varied code styles with pseudo-code to enhance robustness. We evaluate PseudoBridge across 10 PLMs and 6 mainstream programming languages. Extensive experiments demonstrate that PseudoBridge consistently outperforms baselines, achieving significant improvements in generalization, particularly in zero-shot scenarios like Solidity and XLCoST. Extended evaluations using open-source LLMs and advanced embeddings confirm that these gains stem from PseudoBridge's intrinsic design, independent of specific closed-source models. PseudoBridge achieves performance comparable to SOTA embedding methods, highlighting the effectiveness of explicit logical and semantic alignment via pseudo-code as a robust solution for code retrieval.

Lipeng Ma, Weidong Yang, Yixuan Li et al.

Automated log analysis is crucial to ensure high availability and reliability of complex systems. The advent of LLMs in NLP has ushered in a new era of language model-driven automated log analysis, garnering significant interest. Within this field, two primary paradigms based on language models for log analysis have become prominent. Small Language Models (SLMs) follow the pre-train and fine-tune paradigm, focusing on the specific log analysis task through fine-tuning on supervised datasets. On the other hand, LLMs following the in-context learning paradigm, analyze logs by providing a few examples in prompt contexts without updating parameters. Despite their respective strengths, we notice that SLMs are more cost-effective but less powerful, whereas LLMs with large parameters are highly powerful but expensive and inefficient. To trade-off between the performance and inference costs of both models in automated log analysis, this paper introduces an adaptive log analysis framework known as AdaptiveLog, which effectively reduces the costs associated with LLM while ensuring superior results. This framework collaborates an LLM and a small language model, strategically allocating the LLM to tackle complex logs while delegating simpler logs to the SLM. Specifically, to efficiently query the LLM, we propose an adaptive selection strategy based on the uncertainty estimation of the SLM, where the LLM is invoked only when the SLM is uncertain. In addition, to enhance the reasoning ability of the LLM in log analysis tasks, we propose a novel prompt strategy by retrieving similar error-prone cases as the reference, enabling the model to leverage past error experiences and learn solutions from these cases. Extensive experiments demonstrate that AdaptiveLog achieves state-of-the-art results across different tasks, elevating the overall accuracy of log analysis while maintaining cost efficiency.