Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data paired with executable test oracles, and optimization imbalance when handling diverse language pairs.We propose BootTrans, a bootstrapping method that resolves both obstacles.Its key idea is to leverage the functional invariance and cross-lingual portability of test suites, adapting abundant pivot-language unit tests to serve as universal verification oracles for multilingual reinforcement learning (RL) training.Our method introduces a dual-pool architecture with seed and exploration pools to progressively expand training data via executionguided experience collection.Furthermore, we design a language-aware weighting mechanism that dynamically prioritizes harder translation directions based on relative performance across sibling languages, mitigating optimization imbalance.Extensive experiments on the HumanEval-X and TransCoder-Test benchmarks demonstrate substantial improvements over baseline LLMs across all translation directions, with ablation studies validating the effectiveness of both bootstrapping and weighting components.

Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion.Yet, it is challenging for pre-trained LMs to generate correct completions in private repositories.Previous studies retrieve cross-file context based on import relations or text similarity, which is insufficiently relevant to completion targets.In this paper, we propose a dataflow-guided retrieval augmentation approach, called DRACO, for repository-level code completion.DRACO parses a private repository into code entities and establishes their relations through an extended dataflow analysis, forming a repo-specific context graph.Whenever triggering code completion, DRACO precisely retrieves relevant background knowledge from the repo-specific context graph and generates well-formed prompts to query code LMs.Furthermore, we construct a large Python dataset, ReccEval, with more diverse completion targets.Our experiments demonstrate the superior accuracy and applicable efficiency of DRACO, improving code exact match by 3.43% and identifier F1-score by 3.27% on average compared to the state-ofthe-art approach.

Large language models (LLMs) have achieved impressive performance on code generation. Although prior studies enhanced LLMs with prompting techniques and code refinement, they still struggle with complex programming problems due to rigid solution plans. In this paper, we draw on pair programming practices to propose PairCoder, a novel LLM-based framework for code generation. PairCoder incorporates two collaborative LLM agents, namely a Navigator agent for high-level planning and a Driver agent for specific implementation. The Navigator is responsible for proposing promising solution plans, selecting the current optimal plan, and directing the next iteration round based on execution feedback. The Driver follows the guidance of Navigator to undertake initial code generation, code testing, and refinement. This interleaved and iterative workflow involves multi-plan exploration and feedback-based refinement, which mimics the collaboration of pair programmers. We evaluate PairCoder with both open-source and closed-source LLMs on various code generation benchmarks. Extensive experimental results demonstrate the superior accuracy of PairCoder, achieving relative pass@1 improvements of 12.00%--162.43% compared to prompting LLMs directly.