https://github.com/Lizenan1995/DNNOpAcc
공식 코드Boosting Operational DNN Testing Efficiency through Conditioning
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software testing
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MUICT-SERU/MMT4NL
공식 코드Test It Before You Trust It: Applying Software Testing for Trustworthy In-context Learning
https://github.com/Yepkio/sidl
공식 코드https://github.com/am0x00/quantest
공식 코드QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems
https://github.com/amirdeljouyi/UTGen
공식 코드Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests
https://github.com/assert-kth/lollm
공식 코드https://github.com/ast-fortiss-tum/coverage-emse-24
공식 코드Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
https://github.com/ckaestne/seaibib
공식 코드https://github.com/deepsoftwareanalytics/telly
공식 코드Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond
https://github.com/ebadi/scenariogenerator
공식 코드https://github.com/edputh/llms-based-fuzzer-survey
공식 코드On the Challenges of Fuzzing Techniques via Large Language Models
https://github.com/intelligent-cat-lab/seer
공식 코드https://github.com/karpurapus/bddgpt-automate-tests
공식 코드https://github.com/logic-star-ai/swt-bench
공식 코드SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents
https://github.com/marti5ini/gencda
공식 코드Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery
https://github.com/microsoft/intrepid
공식 코드Towards Principled Representation Learning from Videos for Reinforcement Learning
https://github.com/mlsys-seo/FtGAN
공식 코드https://github.com/mosabrezaei/test-case-recommendation
공식 코드Test Case Recommendations with Distributed Representation of Code Syntactic Features
https://github.com/naver/splade
공식 코드https://github.com/npaulinastevia/drl_se
공식 코드A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks
https://github.com/pdedp/interevo-tr
공식 코드InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessment
https://github.com/plasma-umass/coverup
공식 코드https://github.com/plasma-umass/coverup-eval
공식 코드https://github.com/rincloud/trickcatcher
공식 코드LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
https://github.com/schieferdecker/ai4st
공식 코드https://github.com/schieferdecker/ai4stpaper
공식 코드https://github.com/seer-lab/augmentationbias
공식 코드Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models
https://github.com/sherbold/replication-kit-2020-smoke-testing
공식 코드Smoke Testing for Machine Learning: Simple Tests to Discover Severe Defects
https://github.com/sherbold/replication-kit-2022-non-dl-diff-test
공식 코드https://github.com/susperius/icisc_rnnfuzz
공식 코드https://github.com/sycny/Awesome-Trustworthy-GUI-Agents
공식 코드https://github.com/tizpaz/parfait-ml
공식 코드https://github.com/udevops2020/replication-package-reasoning-based-software-testing
공식 코드https://github.com/wolwe1/gp-automated-testing
공식 코드Genetic Micro-Programs for Automated Software Testing with Large Path Coverage
https://github.com/DataScienceNigeria/Research-Review
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
https://github.com/GPflow/GPflow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.