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Collecting relevant and high-quality data is integral to the development ofeffective Software Vulnerability (SV) prediction models. Most of the current SVdatasets rely on SV-fixing commits to extract vulnerable functions and lines.However, none of these datasets have considered latent SVs existing between theintroduction and fix of the collected SVs. There is also little known about theusefulness of these latent SVs for SV prediction. To bridge these gaps, weconduct a large-scale study on the latent vulnerable functions in two commonlyused SV datasets and their utilization for function-level and line-level SVpredictions. Leveraging the state-of-the-art SZZ algorithm, we identify morethan 100k latent vulnerable functions in the studied datasets. We find thatthese latent functions can increase the number of SVs by 4x on average andcorrect up to 5k mislabeled functions, yet they have a noise level of around6%. Despite the noise, we show that the state-of-the-art SV prediction modelcan significantly benefit from such latent SVs. The improvements are up to24.5% in the performance (F1-Score) of function-level SV predictions and up to67% in the effectiveness of localizing vulnerable lines. Overall, our studypresents the first promising step toward the use of latent SVs to improve thequality of SV datasets and enhance the performance of SV prediction tasks.

Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Study