Challenge: Existing methods to generate test cases using large language models are limited in their ability to generate unit test cases.
Approach: They propose a test case generation benchmark that uses large language models to generate unit test cases.
Outcome: The proposed test case generation benchmarks compare LLMs with commercial and open-source LLM platforms and find that they lack the ability to comprehend program logic and execution paths.

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Challenge: Existing benchmarks focused on simplified or isolated aspects of coding, ignoring the full spectrum of programming challenges.
Approach: They propose a case study that examines the performance of large language models across the entire software development lifecycle with four programming languages, multiple domains, and carefully designed and verified metrics for each task.
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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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Can LLMs Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverage and Exposure (2025.acl-short)

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Challenge: TestCase-Eval focuses on Fault Coverage and Fault Exposure tasks . authors provide insights into their strengths and limitations in generating effective test cases . correctness and robustness of algorithmic solutions hinge on quality of test suites .
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Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics (2026.findings-acl)

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Challenge: Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations, limiting their effectiveness for discovering subtle bugs and security vulnerabilities.
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Challenge: Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
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CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation (2026.findings-acl)

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Challenge: Existing approaches to synthesize test cases using Large Language Models (LLMs) rely on the model’s intrinsic generation capabilities without external feedback, resulting in insufficiently diverse cases.
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PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs (2024.findings-emnlp)

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Challenge: *HumanEval* and *MBPP* are two popular benchmarks for Python code generation.
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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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GlotEval: A Test Suite for Massively Multilingual Evaluation of Large Language Models (2025.emnlp-demos)

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Challenge: Existing evaluation frameworks focus on English and a handful of high-resource languages, thereby overlooking the realistic performance of large language models in multilingual and lower-resourced scenarios.
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CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding? (2025.coling-main)

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Challenge: Recent advances in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks.
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