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Meta Learning for Code Summarization

arXiv 2201.08310 ↗First posted 20 Jan 2022cs.LG cs.PL cs.SEcited by 0

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Abstract (arXiv)

Source code summarization is the task of generating a high-level natural language description for a segment of programming language code. Current neural models for the task differ in their architecture and the aspects of code they consider. In this paper, we show that three SOTA models for code summarization work well on largely disjoint subsets of a large code-base. This complementarity motivates model combination: We propose three meta-models that select the best candidate summary for a given code segment. The two neural models improve significantly over the performance of the best individual model, obtaining an improvement of 2.1 BLEU points on a dataset of code segments where at least one of the individual models obtains a non-zero BLEU.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 20 Jan 2022.

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References · 12

  1. 2020SummEval: Re-evaluating Summarization Evaluationcited by 674
  2. 2020A Transformer-based Approach for Source Code Summarizationcited by 133
  3. 2018Improving Automatic Source Code Summarization via Deep Reinforcement Learningcited by 100
  4. 2020Improved Code Summarization via a Graph Neural Networkcited by 79
  5. 2019A Neural Model for Generating Natural Language Summaries of Program Subroutinescited by 77
  6. 2019Code Generation as a Dual Task of Code Summarizationcited by 62
  7. 2019Recommendations for Datasets for Source Code Summarizationcited by 46
  8. 2020Improved Automatic Summarization of Subroutines via Attention to File Contextcited by 34
  9. 2021Project-Level Encoding for Neural Source Code Summarization of Subroutinescited by 24
  10. 2020Dissecting Span Identification Tasks with Performance Predictioncited by 13
  11. 2021Ensemble Models for Neural Source Code Summarization of Subroutinescited by 12
  12. 2019Automatically Extracting Subroutine Summary Descriptions from Unstructured Commentscited by 1

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