Active code learning: Benchmarking sample-efficient training of code models

• Main Authors: HU, Qiang, GUO, Yuejun, XIE, Xiaofei, CORDY, Maxime, MA, Lei, PAPADAKIS, Mike, TRAON, Yves Le
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Codes; Data Models; Task Analysis; Training; Feature Extraction; Training Data; Labeling; Active Learning; Machine Learning For Code; Benchmark; Empirical Analysis; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8695; https://ink.library.smu.edu.sg/context/sis_research/article/9698/viewcontent/ActiveCodeLearning_av.pdf
• Institution: Singapore Management University

The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), especially for those with limited budgets. Therefore, efficiently training models of code with less human effort has become a...