Challenge: Despite the critical role of software requirements, these criteria have not been studied actively in previous code generation works.
Approach: They propose a framework that leverages in-context learning to organize and extrapolate unexpressed requirements from textual descriptions.
Outcome: The proposed framework generates functional requirements from textual descriptions and extrapolates unexpressed requirements from them.

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Challenge: Large Language Models (LLMs) have demonstrated potential in code generation and natural language understanding, but they struggle with code constraints.
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CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation (2025.acl-industry)

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Challenge: CodeIF assesses the ability of large language models to adhere to task-oriented instructions in code generation tasks.
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ToolCoder: A Systematic Code-Empowered Tool Learning Framework for Large Language Models (2025.acl-long)

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Challenge: Existing approaches to tool learning rely on hand-crafted prompts and natural language reasoning, making multi-step planning difficult and lacking precise error diagnosis and reflection mechanisms.
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Evaluating In-Context Learning of Libraries for Code Generation (2024.naacl-long)

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Challenge: Recent work shows that large proprietary LLMs can learn novel library usage in-context from demonstrations.
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DocCGen: Document-based Controlled Code Generation (2024.emnlp-main)

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Challenge: Large language models (LLMs) produce state-of-the-art performance on natural language to code generation for resource-rich general-purpose languages like C++, Java, and Python.
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XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval (2024.acl-long)

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Challenge: Recent advances in large language models have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments.
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Flow2Code: Evaluating Large Language Models for Flowchart-based Code Generation Capability (2025.findings-acl)

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Challenge: Existing code generation benchmarks neglect flowchart-based code generation . existing benchmarks lack flowcharting-based evaluation, limiting the potential of large language models and minimizing human error.
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Large Language Models Meet NL2Code: A Survey (2023.acl-long)

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Challenge: generating code from a natural language description is a pressing and significant challenge in code intelligence.
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mHumanEval - A Multilingual Benchmark to Evaluate Large Language Models for Code Generation (2025.naacl-long)

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Challenge: Current evaluations focus on English-to-Python conversion tasks with limited test cases . code generation from low-resource language prompts remains largely unexplored .
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The Program Testing Ability of Large Language Models for Code (2024.emnlp-industry)

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Challenge: Recent development of large language models (LLMs) for code shows promise in achieving code intelligence.
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