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Systematic Literature Review
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/FlowSs/How-to-Certify-Machine-Learning-BasedSafety-critical-Systems-A-Systematic-Literature-Review
공식 코드How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review
https://github.com/Jasper0122/BuildingView
공식 코드https://github.com/VaryVary/ML-configurable-SLR
공식 코드Learning Software Configuration Spaces: A Systematic Literature Review
https://github.com/Victor-Morel/SLR_AI_PPA
공식 코드AI-driven Personalized Privacy Assistants: a Systematic Literature Review
https://github.com/anonsub1234/ptm-quantify-esem-2024
공식 코드https://github.com/aramis-lab/AD-DL
공식 코드https://github.com/bean-young/ai4med
공식 코드https://github.com/bean-young/ai4radiology
공식 코드https://github.com/booktrackerGirl/Sys-lit-review-Sexism
공식 코드https://github.com/cm-awais/sdp_nb_rf_meta_analysis
공식 코드A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction
https://github.com/comp-journalism/list-of-algorithm-audits
공식 코드Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits
https://github.com/dishacse/Publication-Resources/tree/main/2025%20ICSA
공식 코드https://github.com/dlt-science/ESG-DLT-LitReview
공식 코드Evolution of ESG-focused DLT Research: An NLP Analysis of the Literature
https://github.com/edisutoyo/satd-detection-slr
공식 코드Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review
https://github.com/eramam/predictivemonitoringdatasets
공식 코드Deep Learning for Predictive Business Process Monitoring: Review and Benchmark
https://github.com/facebookresearch/xR-EgoPose
공식 코드https://github.com/fastread/src
공식 코드Finding Better Active Learners for Faster Literature Reviews
https://github.com/furrutiav/nllf-emnlp-2023
공식 코드Deep Natural Language Feature Learning for Interpretable Prediction
https://github.com/greek-nlp/survey
공식 코드Towards Systematic Monolingual NLP Surveys: GenA of Greek NLP
https://github.com/hasan-rakibul/boolean-search-bib-abstract
공식 코드https://github.com/hiaac-finance/credit_pipeline
공식 코드Best Practices for Responsible Machine Learning in Credit Scoring
https://github.com/hiyouga/llama-efficient-tuning
공식 코드Large Language Models for Cyber Security: A Systematic Literature Review
https://github.com/ielab/goldilocks-reproduce
공식 코드A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR
https://github.com/jonas-becker/text-generation
공식 코드Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges
https://github.com/juyongjiang/codellmsurvey
공식 코드https://github.com/kiyan-rezaee/systematic-literature-review-on-online-continual-learning
공식 코드Online Continual Learning: A Systematic Literature Review of Approaches, Challenges, and Benchmarks
https://github.com/media-bias-group/media-bias-taxonomy
공식 코드https://github.com/poclecoqq/slr-datacleaning
공식 코드Data Cleaning and Machine Learning: A Systematic Literature Review
https://github.com/projectdossier/citationscreeningreplicability
공식 코드https://github.com/purduedualitylab/ptm-quantify-esem-2024
공식 코드https://github.com/rosaliatufano/impact_pre-training
공식 코드Automating Code-Related Tasks Through Transformers: The Impact of Pre-training
https://github.com/rrrrrrockpang/llm-chi
공식 코드https://github.com/s2e-lab/llm-education-survey
공식 코드Large Language Models in Computer Science Education: A Systematic Literature Review
https://github.com/sgholamian/comprehensive-software-logging-survey
공식 코드https://github.com/sm2982/meta-analysis-llms-creativity
공식 코드Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.