Data flow coverage criteria are widely used in software testing, but there is almost no research on low-level data flow metrics as software defect predictors. Aims: We examine two such metrics in this context: dep-degree (DD) proposed by Beyer and Fararooy and a new data flow metric called dep-degree density (DDD).Method: We investigate the importance of DD and DDD in SDP models. We perform a correlation analysis to check if DD and DDD measure different aspects of the code than the well-known size, complexity, and documentation metrics. Finally, we perform experiments with five different classifiers on nine projects from the Unified Bug Dataset to compare the performance of the SDP models trained with and without data flow metrics. Results: 1) DD is noticeably correlated with many other code metrics, but DDD is not correlated or is very weakly correlated with other metrics considered in this study; 2) both DD and DDD are highly ranked in the feature importance analysis; 3) SDP models that use DD and DDD perform better than models that do not use data flow metrics. Conclusions: Data-flow metrics: DD and DDD can be valuable predictors in SDP models.