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Multi-granular software annotation using file-level weak labelling

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Abstract

Context

One of the most time-consuming tasks for developers is the comprehension of new code bases. An effective approach to aid this process is to label source code files with meaningful annotations, which can help developers understand the content and functionality of a code base quicker. However, most existing solutions for code annotation focus on project-level classification: manually labelling individual files is time-consuming, error-prone and hard to scale.

Objective

The work presented in this paper aims to automate the annotation of files by leveraging project-level labels; and using the file-level annotations to annotate items at larger levels of granularity, for example, packages and a whole project.

Method

We propose a novel approach to annotate source code files using a weak labelling approach and a subsequent hierarchical aggregation. We investigate whether this approach is effective in achieving multi-granular annotations of software projects, which can aid developers in understanding the content and functionalities of a code base more quickly.

Results

Our evaluation uses a combination of human assessment and automated metrics to evaluate the annotations’ quality. Our approach correctly annotated 50% of files and more than 50% of packages. Moreover, the information captured at the file-level allowed us to identify, on average, three new relevant labels for any given project. We can conclude that the proposed approach is a convenient and promising way to generate noisy (not precise) annotations for files. Furthermore, hierarchical aggregation effectively preserves the information captured at file-level, and it can be propagated to packages and the overall project itself.

Conclusions

We can conclude that the proposed approach is a convenient and promising way to generate noisy (not precise) annotations for files. Furthermore, hierarchical aggregation effectively preserves the information captured at file-level, and it can be propagated to packages and the overall project itself.

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Data Availability Statement

The dataset used and generated artefacts are available in a Zenodo repository: https://zenodo.org/record/7943882. The code is available at the following repository: https://github.com/SasCezar/CodeGraphClassification

Notes

  1. https://github.com/Waikato/weka-3.8

  2. https://github.com/mickleness/pumpernickel

  3. https://github.com/SasCezar/CodeGraphClassification

  4. https://zenodo.org/record/7943882

  5. https://github.com/tree-sitter/tree-sitter

  6. In natural language, a hypernym describes a broader term, whereas a hyponym is a more specialised word. For example, ‘Deep Learning’ is the hypernym, while ‘Convolutional Neural Network (or ‘CNN’) is the hyponym.

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  1. Bernoulli Institute, University of Groningen, Groningen, The Netherlands

    Cezar Sas & Andrea Capiluppi

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Sas, C., Capiluppi, A. Multi-granular software annotation using file-level weak labelling. Empir Software Eng 29, 12 (2024). https://doi.org/10.1007/s10664-023-10423-7

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