Alikhashashneh, Enas A.Raje, Rajeev R.Hill, James H.2019-11-152019-11-152018-10Alikhashashneh, E. A., Raje, R. R., & Hill, J. H. (2018). Using Machine Learning Techniques to Classify and Predict Static Code Analysis Tool Warnings. 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), 1–8. https://doi.org/10.1109/AICCSA.2018.8612819https://hdl.handle.net/1805/21358This paper discusses our work on using software engineering metrics (i.e., source code metrics) to classify an error message generated by a Static Code Analysis (SCA) tool as a true-positive, false-positive, or false-negative. Specifically, we compare the performance of Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forests, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) over eight datasets. The performance of the techniques is assessed by computing the F-measure metric, which is defined as the weighted harmonic mean of the precision and recall of the predicted model. The overall results of the study show that the F-measure value of the predicted model, which is generated using Random Forests technique, ranges from 83% to 98%. Additionally, the Random Forests technique outperforms the other techniques. Lastly, our results indicate that the complexity and coupling metrics have the most impact on whether a SCA tool with generate a false-positive warning or not.enPublisher Policystatic code analysissoftware engineeringmetricsArticle