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3.3 Static Analysis Metrics

Metrics derived from static analysis results are useful for prioritizing remediation efforts, allocating resources among multiple projects, and getting feedback on the effectiveness of the security process. Ideally, one could use metrics derived from static analysis results to help quantify the amount of risk associated with a piece of code, but using tools to measure risk is tricky. The most obvious problem is the unshakable presence of false positives and false negatives, but it is possible to compensate for them. By manually auditing enough results, a security team can predict the rate at which false positives and false negatives occur for a given project and extrapolate the number of true positives from a set of raw results. A deeper problem with using static analysis to quantify risk is that there is no good way to sum up the risk posed by a set of vulnerabilities. Are two buffer overflows twice as risky as a single buffer overflow? What about ten? Code-level vulnerabilities identified by tools simply do not sum into an accurate portrayal of risk. See the sidebar "The Density Deception" to understand why.

Instead of trying to use static analysis output to directly quantify risk, use it as a tactical way to focus security efforts and as an indirect measure of the process used to create the code.

Metrics for Tactical Focus

Many simple metrics can be derived from static analysis results. Here we look at the following:

  • Measuring vulnerability density
  • Comparing projects by severity
  • Breaking down results by category
  • Monitoring trends

Measuring Vulnerability Density

We've already thrown vulnerability density under the bus, so what more is there to talk about? Dividing the number of static analysis results by the number of lines of code is an awful way to measure risk, but it's a good way to measure the amount of work required to do a complete review. Comparing vulnerability density across different modules or different projects helps formulate review priorities. Track issue density over time to gain insight into whether tool output is being taken into consideration.

Comparing Projects by Severity

Static analysis results can be applied for project comparison purposes. Figure 3.3 shows a comparison between two modules, with the source code analysis results grouped by severity. The graph suggests a plan of action: Check out the critical issues for the first module, and then move on to the high-severity issues for the second.

Figure 3.3

Figure 3.3 Source code analysis results broken down by severity for two subprojects.

Comparing projects side by side can help people understand how much work they have in front of them and how they compare to their peers. When you present project comparisons, name names. Point fingers. Sometimes programmers need a little help accepting responsibility for their code. Help them.

Breaking Down Results by Category

Figure 3.4 presents results for a single project grouped by category. The pie chart gives a rough idea about the amount of remediation effort required to address each type of issue. It also suggests that log forging and cross-site scripting are good topics for an upcoming training class.

Figure 3.4

Figure 3.4 Source code analysis results broken down by category.

Source code analysis results can also point out trends over time. Teams that are focused on security will decrease the number of static analysis findings in their code. A sharp increase in the number of issues found deserves attention. Figure 3.5 shows the number of issues found during a series of nightly builds. For this particular project, the number of issues found on February 2 spikes because the development group has just taken over a module from a group that has not been focused on security.

Figure 3.5

Figure 3.5 Source code analysis results from a series of nightly builds. The spike in issues on February 2 reflects the incorporation of a module originally written by a different team.

Process Metrics

The very presence of some types of issues can serve as an early indicator of more widespread security shortcomings [Epstein, 2006]. Determining the kinds of issues that serve as bellwether indicators requires some experience with the particular kind of software being examined. In our experience, a large number of string-related buffer overflow issues is a sign of trouble for programs written in C.

More sophisticated metrics leverage the capacity of the source code analyzer to give the same issue the same identifier across different builds. (See Chapter 4, "Static Analysis Internals," for more information on issue identifiers.) By following the same issue over time and associating it with the feedback provided by a human auditor, the source code analyzer can provide insight into the evolution of the project. For example, static analysis results can reveal the way a development team responds to security vulnerabilities. After an auditor identifies a vulnerability, how long, on average, does it take for the programmers to make a fix? We call this vulnerability dwell. Figure 3.6 shows a project in which the programmers fix critical vulnerabilities within two days and take progressively longer to address less severe problems.

Figure 3.6

Figure 3.6 Vulnerability dwell as a function of severity. When a vulnerability is identified, vulnerability dwell measures how long it remains in the code. (The x-axis uses a log scale.)

Static analysis results can also help a security team decide when it's time to audit a piece of code. The rate of auditing should keep pace with the rate of development. Better yet, it should keep pace with the rate at which potential security issues are introduced into the code. By tracking individual issues over time, static analysis results can show a security team how many unreviewed issues a project contains. Figure 3.7 presents a typical graph. At the point the project is first reviewed, audit coverage goes to 100%. Then, as the code continues to evolve, the audit coverage decays until the project is audited again.

Figure 3.7

Figure 3.7 Audit coverage over time. After all static analysis results are reviewed, the code continues to evolve and the percentage of reviewed issues begins to decline.

Another view of this same data gives a more comprehensive view of the project. An audit history shows the total number of results, number of results reviewed, and number of vulnerabilities identified in each build. This view takes into account not just the work of the code reviewers, but the effect the programmers have on the project. Figure 3.8 shows results over roughly one month of nightly builds. At the same time the code review is taking place, development is in full swing, so the issues in the code continue to change. As the auditors work, they report vulnerabilities (shown in black).

Figure 3.8

Figure 3.8 Audit history: the total number of static analysis results, the number of reviewed results, and the number of identified vulnerabilities present in the project.

Around build 14, the auditors have looked at all the results, so the total number of results is the same as the number reviewed. Development work is not yet complete, though, and soon the project again contains unreviewed results. As the programmers respond to some of the vulnerabilities identified by the audit team, the number of results begins to decrease and some of the identified vulnerabilities are fixed. At the far-right side of the graph, the growth in the number of reviewed results indicates that reviewers are beginning to look at the project again.

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