# A Manually-Curated Dataset of Fixes to Vulnerabilities of Open-Source Software

## TL;DR

This paper presents a manually curated, open-source dataset linking 624 vulnerabilities in Java projects to 1282 fixing commits, supporting research in automated vulnerability detection and mitigation.

## Contribution

It introduces a new, open-source dataset of vulnerabilities and fixes in open-source Java projects, including scripts for data augmentation and retrieval.

## Key findings

- Dataset covers vulnerabilities with and without CVE identifiers.
- Scripts enable augmentation with non-security commits.
- Dataset has been used to train classifiers for security-relevant commit detection.

## Abstract

Advancing our understanding of software vulnerabilities, automating their identification, the analysis of their impact, and ultimately their mitigation is necessary to enable the development of software that is more secure. While operating a vulnerability assessment tool that we developed and that is currently used by hundreds of development units at SAP, we manually collected and curated a dataset of vulnerabilities of open-source software and the commits fixing them. The data was obtained both from the National Vulnerability Database (NVD) and from project-specific Web resources that we monitor on a continuous basis. From that data, we extracted a dataset that maps 624 publicly disclosed vulnerabilities affecting 205 distinct open-source Java projects, used in SAP products or internal tools, onto the 1282 commits that fix them. Out of 624 vulnerabilities, 29 do not have a CVE identifier at all and 46, which do have a CVE identifier assigned by a numbering authority, are not available in the NVD yet. The dataset is released under an open-source license, together with supporting scripts that allow researchers to automatically retrieve the actual content of the commits from the corresponding repositories and to augment the attributes available for each instance. Also, these scripts allow to complement the dataset with additional instances that are not security fixes (which is useful, for example, in machine learning applications). Our dataset has been successfully used to train classifiers that could automatically identify security-relevant commits in code repositories. The release of this dataset and the supporting code as open-source will allow future research to be based on data of industrial relevance; also, it represents a concrete step towards making the maintenance of this dataset a shared effort involving open-source communities, academia, and the industry.