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- W2993894959 abstract "This 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." @default.
- W2993894959 created "2019-12-13" @default.
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- W2993894959 date "2018-10-01" @default.
- W2993894959 modified "2023-09-26" @default.
- W2993894959 title "Using Machine Learning Techniques to Classify and Predict Static Code Analysis Tool Warnings" @default.
- W2993894959 hasPublicationYear "2018" @default.
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