This paper presents our results from identifying anddocumenting false positives generated by static code analysistools. By false positives, we mean a static code analysis toolgenerates a warning message, but the warning message isnot really an error. The goal of our study is to understandthe different kinds of false positives generated so we can (1)automatically determine if an error message is truly indeed a truepositive, and (2) reduce the number of false positives developersand testers must triage. We have used two open-source tools andone commercial tool in our study. The results of our study haveled to 14 core false positive patterns, some of which we haveconfirmed with static code analysis tool developers.