MADE-WIC: Multiple Annotated Datasets for Exploring Weaknesses In Code

In this paper, we present MADE-WIC, a large dataset of functions and their comments with multiple annotations for technical debt and code weaknesses leveraging different state-of-the-art approaches. It contains about 860K code functions and more than 2.7M related comments from 12 open-source projects. To the best of our knowledge, no such dataset is publicly available. MADE-WIC aims to provide researchers with a curated dataset on which to test and compare tools designed for the detection of code weaknesses and technical debt. As we have fused existing datasets, researchers have the possibility to evaluate the performance of their tools by also controlling the bias related to the annotation definition and dataset construction. The demonstration video can be retrieved at https://www.youtube.com/watch?v=GaQodPrcb6E.

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Publication Details

Published
2024-10-18
DOI
https://doi.org/10.1145/3691620.3695348
Citations
4
Primary Topic
Software Engineering Research
Type
article
Field-Weighted Citation Impact
3.12
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article

MADE-WIC: Multiple Annotated Datasets for Exploring Weaknesses In Code

Barbara Russo, Jorge Melegati, Nicolás E. Díaz Ferreyra, Moritz Mock et al.
4 citations
Software Engineering Research
3.12
article

MADE-WIC: Multiple Annotated Datasets for Exploring Weaknesses In Code

Barbara Russo, Jorge Melegati, Nicolás E. Díaz Ferreyra, Moritz Mock, Max Kretschmann
article en
4 citations

Abstract

In this paper, we present MADE-WIC, a large dataset of functions and their comments with multiple annotations for technical debt and code weaknesses leveraging different state-of-the-art approaches. It contains about 860K code functions and more than 2.7M related comments from 12 open-source projects. To the best of our knowledge, no such dataset is publicly available. MADE-WIC aims to provide researchers with a curated dataset on which to test and compare tools designed for the detection of code weaknesses and technical debt. As we have fused existing datasets, researchers have the possibility to evaluate the performance of their tools by also controlling the bias related to the annotation definition and dataset construction. The demonstration video can be retrieved at https://www.youtube.com/watch?v=GaQodPrcb6E.

Universität Hamburg (DE), Free University of Bozen-Bolzano (IT), Hamburg University of Technology (DE)
Openalex Percentile: Top 7%
Software Engineering Research
3.12
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