Work

Using Machine Learning Techniques to Classify and Predict Static Code Analysis Tool Warnings

Year: 2018

Type: conference-paper

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.

Source: IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

DOI: 10.1109/aiccsa.2018.8612819

Authors: Enas A. Alikhashashneh, Rajeev R. Raje, James H. Hill

Institution: Indiana University – Purdue University Indianapolis

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: U.S. Department of Homeland Security

Award: G6840302281