PropGen automates property generation for Android app testing via LLM synthesis from guided exploration and feedback refinement, yielding 912 valid properties and 25 previously unknown bugs across 12 apps.
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Unit test case generation with transformers and focal context
13 Pith papers cite this work. Polarity classification is still indexing.
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FixAudit improves LLM code generation on competitive programming benchmarks by training a shared model for iterative code-aware test generation and repair, achieving 35%+ gains in Pass@1 over baselines on the same 7B model.
CodeT improves code generation accuracy by using the same model to create test cases and then selecting solutions via output agreement on those tests, raising HumanEval pass@1 from 47% to 65.8%.
FeedbackLLM uses line and branch coverage feedback agents in an iterative multi-agent process with a redundancy cache to generate test cases achieving higher coverage than baselines on standard C and Python benchmarks while scaling linearly in time.
TestGeneralizer generalizes an initial test into a set of executable tests covering more diverse scenarios, delivering +31.66% mutation-based and +23.08% LLM-assessed scenario coverage gains over ChatTester on 12 open-source Java projects.
SpecTune improves LLM-based automated program repair by deriving localized postconditions at execution checkpoints and using alpha and beta signals to produce precise fault-localization and patch-generation guidance.
By proving test suite coverage is monotone submodular and training LLMs with RL to maximize marginal gains, TestDecision improves branch coverage 38-52% and bug detection up to 95% over base models on ULT and LiveCodeBench.
IntentionTest retrieves a reusable test from the project and edits it with an LLM to match a supplied validation intention, yielding tests that kill 28.1-37.6% more mutants, share 16.9-23.9% more coverage, and produce 23.7-49.0% more passing tests than four baselines on 3,680 cases.
PGS generates property-oriented, structurally minimal feedback from high-level program properties to refine LLM code, yielding up to 13.4% pass@1 gains and 1.4-1.6x higher bug-fix rates than prior TDD and debugging baselines.
CodeXGLUE supplies a standardized collection of 10 code-related tasks, 14 datasets, an evaluation platform, and BERT-, GPT-, and encoder-decoder-style baselines.
PPO-LLM adaptively selects among eight prompting techniques using an 11-dimensional state vector to guide an LLM toward higher branch and line coverage than static baselines on 20 benchmark programs.
StarCoderBase matches or beats OpenAI's code-cushman-001 on multi-language code benchmarks; the Python-fine-tuned StarCoder reaches 40% pass@1 on HumanEval while retaining other-language performance.
CodePori is a multi-agent LLM system for code generation whose participant evaluation identifies practical challenges like memory limits and hallucinations missed by binary benchmarks.
citing papers explorer
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From Exploration to Specification: LLM-Based Property Generation for Mobile App Testing
PropGen automates property generation for Android app testing via LLM synthesis from guided exploration and feedback refinement, yielding 912 valid properties and 25 previously unknown bugs across 12 apps.
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An Iterative Test-and-Repair Framework for Competitive Code Generation
FixAudit improves LLM code generation on competitive programming benchmarks by training a shared model for iterative code-aware test generation and repair, achieving 35%+ gains in Pass@1 over baselines on the same 7B model.
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CodeT: Code Generation with Generated Tests
CodeT improves code generation accuracy by using the same model to create test cases and then selecting solutions via output agreement on those tests, raising HumanEval pass@1 from 47% to 65.8%.
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FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback
FeedbackLLM uses line and branch coverage feedback agents in an iterative multi-agent process with a redundancy cache to generate test cases achieving higher coverage than baselines on standard C and Python benchmarks while scaling linearly in time.
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Generalizing Test Cases for Comprehensive Test Scenario Coverage
TestGeneralizer generalizes an initial test into a set of executable tests covering more diverse scenarios, delivering +31.66% mutation-based and +23.08% LLM-assessed scenario coverage gains over ChatTester on 12 open-source Java projects.
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Enhancing Program Repair with Specification Guidance and Intermediate Behavioral Signals
SpecTune improves LLM-based automated program repair by deriving localized postconditions at execution checkpoints and using alpha and beta signals to produce precise fault-localization and patch-generation guidance.
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TestDecision: Sequential Test Suite Generation via Greedy Optimization and Reinforcement Learning
By proving test suite coverage is monotone submodular and training LLMs with RL to maximize marginal gains, TestDecision improves branch coverage 38-52% and bug detection up to 95% over base models on ULT and LiveCodeBench.
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Generating Project-Specific Test Cases with Requirement Validation Intention
IntentionTest retrieves a reusable test from the project and edits it with an LLM to match a supplied validation intention, yielding tests that kill 28.1-37.6% more mutants, share 16.9-23.9% more coverage, and produce 23.7-49.0% more passing tests than four baselines on 3,680 cases.
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Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback
PGS generates property-oriented, structurally minimal feedback from high-level program properties to refine LLM code, yielding up to 13.4% pass@1 gains and 1.4-1.6x higher bug-fix rates than prior TDD and debugging baselines.
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CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
CodeXGLUE supplies a standardized collection of 10 code-related tasks, 14 datasets, an evaluation platform, and BERT-, GPT-, and encoder-decoder-style baselines.
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PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation
PPO-LLM adaptively selects among eight prompting techniques using an 11-dimensional state vector to guide an LLM toward higher branch and line coverage than static baselines on 20 benchmark programs.
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StarCoder: may the source be with you!
StarCoderBase matches or beats OpenAI's code-cushman-001 on multi-language code benchmarks; the Python-fine-tuned StarCoder reaches 40% pass@1 on HumanEval while retaining other-language performance.
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CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology
CodePori is a multi-agent LLM system for code generation whose participant evaluation identifies practical challenges like memory limits and hallucinations missed by binary benchmarks.