On the capability of static code analysis to detect security vulnerabilities

Abstract

Context:Static analysis of source code is a scalable method for discovery of software faults and security vulnerabilities. Techniques for static code analysis have matured in the last decade and many tools have been developed to support automatic detection.Objective: This research work is focused on empirical evaluation of the ability of static code analysis tools to detect security vulnerabilities with an objective to better understand their strengths and shortcomings.Method: We conducted an experiment which consisted of using the benchmarking test suite Juliet to evaluate three widely used commercial tools for static code analysis. Using design of experiments approach to conduct the analysis and evaluation and including statistical testing of the results are unique characteristics of this work. In addition to the controlled experiment, the empirical evaluation included case studies based on three open source programs.Results: Our experiment showed that 27% of C/C++ vulnerabilities and 11% of Java vulnerabilities were missed by all three tools. Some vulnerabilities were detected by only one or combination of two tools; 41% of C/C++ and 21% of Java vulnerabilities were detected by all three tools. More importantly, static code analysis tools did not show statistically significant difference in their ability to detect security vulnerabilities for both C/C++ and Java. Interestingly, all tools had median and mean of the per CWE recall values and overall recall across all CWEs close to or below 50%, which indicates comparable or worse performance than random guessing. While for C/C++ vulnerabilities one of the tools had better performance in terms of probability of false alarm than the other two tools, there was no statistically significant difference among tools’ probability of false alarm for Java test cases.Conclusions: Despite recent advances in methods for static code analysis, the state-of-the-art tools are not very effective in detecting security vulnerabilities.