# Data Preparation for Software Vulnerability Prediction: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2109.05740 · HTML: https://mlsanity.com/paper/2109.05740/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2021.

## Built on by (most cited first, 5 of 5)

- [Improving Automated Program Repair with Domain Adaptation](https://mlsanity.com/paper/2212.11414/) (2022)
- [When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection](https://mlsanity.com/paper/2308.10523/) (2023)
- [XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection](https://mlsanity.com/paper/2309.14677/) (2023)
- [MVD: A Multi-Lingual Software Vulnerability Detection Framework](https://mlsanity.com/paper/2412.06166/) (2024)
- [An Empirical Study of the Imbalance Issue in Software Vulnerability Detection](https://mlsanity.com/paper/2602.12038/) (2026)

## Abstract (arXiv)

Software Vulnerability Prediction (SVP) is a data-driven technique for software quality assurance that has recently gained considerable attention in the Software Engineering research community. However, the difficulties of preparing Software Vulnerability (SV) related data is considered as the main barrier to industrial adoption of SVP approaches. Given the increasing, but dispersed, literature on this topic, it is needed and timely to systematically select, review, and synthesize the relevant peer-reviewed papers reporting the existing SV data preparation techniques and challenges. We have carried out a Systematic Literature Review (SLR) of SVP research in order to develop a systematized body of knowledge of the data preparation challenges, solutions, and the needed research. Our review of the 61 relevant papers has enabled us to develop a taxonomy of data preparation for SVP related challenges. We have analyzed the identified challenges and available solutions using the proposed taxonomy. Our analysis of the state of the art has enabled us identify the opportunities for future research. This review also provides a set of recommendations for researchers and practitioners of SVP approaches.