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Comparing the Pretrained Models of Source Code by Re-pretraining Under a Unified Setup.

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    Large pretrained models of source code (CodePTMs) advance software engineering. This study standardizes experiments to fairly compare CodePTMs and analyze pretraining task effectiveness for future model development.

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    Area of Science:

    • Computer Science
    • Software Engineering
    • Artificial Intelligence

    Background:

    • Large pretrained models of source code (CodePTMs) have shown success in code representation learning.
    • These models have shifted software engineering towards task-agnostic solutions.
    • Existing CodePTMs lack direct comparability due to varied experimental setups.

    Purpose of the Study:

    • To establish a standardized experimental setup for fair comparison of CodePTMs.
    • To investigate the impact of different pretraining tasks on CodePTM performance.
    • To provide insights for developing more powerful CodePTMs.

    Main Methods:

    • Reviewed existing experimental setups for CodePTMs.
    • Proposed and implemented a standardized setup for pretraining and evaluation.
    • Re-pretrained CodePTMs with consistent architecture, modalities, and tasks.
    • Fine-tuned each model on various software engineering tasks.

    Main Results:

    • Presented experimental results comparing CodePTMs under the standardized setup.
    • Discussed the relative strengths and weaknesses of different pretraining tasks across SE tasks.
    • Identified key factors influencing CodePTM performance.

    Conclusions:

    • Standardized comparisons are crucial for advancing CodePTM research.
    • Understanding pretraining task impacts is vital for future model development.
    • This work provides a foundation for more powerful and comparable CodePTMs.