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Human Factors in Evaluating Static Analysis Tools


Advanced static analysis tools are popular because they have proven effective at finding serious programming defects. In contrast to traditional dynamic testing, the code is never executed so there is no need for test cases. This means that static analysis can be applied very early in the development process. When programmers use static analysis as soon as code is written, bugs and security vulnerabilities can be found and eliminated even before the unit testing or integration testing phases. The earlier a defect is found, the cheaper it is to fix. This cost saving is a major advantage of automated static analysis.

The latest static analysis tools represent a significant technological improvement on early generation tools. These new tools use sophisticated whole program techniques to find defects and are typically used on large quantities of source code written in high-level languages such as C/C++ and Java, or even on object code. Such tools are the subject of this post.



Of course these tools are not perfect. For most non-trivial programs no practical tool can find all bugs (i.e., there are false negatives), and all such tools may also report problems in code that is defect free (i.e., false positives). As I will explain later, given the technology and resources generally available today, this is inevitable. Because of this, all warnings from a tool must be inspected by a human to determine whether or not they indicate a real problem, and whether they warrant action. This process is known as triage.

There is a strong inverse relationship between false positives and false negatives. Tools that find more real defects (true positives) generally also have a higher rate of false positives. Even for a single tool, there are typically configuration options that allow end users to control the underlying analysis to favor one end of the spectrum over the other.

Users dislike false positive results for various reasons, so they prefer tools (or configurations of tools) that keep the false positive rate low. However, this comes with the risk that the tool might fail to report real defects. The most effective tool is one that strikes the right balance between false positives, false negatives, and performance. When deploying one of these tools, or deciding which tool to purchase, users should consider this tradeoff to maximize the benefit of using the tool. There are lots of factors that influence this decision. In a later post, I will describe the important considerations and parameters, then describe a model that can help users measure the benefit of a static analysis tool and assess how best to configure it. Equations derived from the model allow users to compare tools using simple warning report counts.


First I would like to briefly introduce some terminology. Static analysis tools are essentially detectors of defects, so some of the vocabulary from information retrieval is appropriate here. Recall is a measure of the ability of a tool to find real defects. It is defined as the probability that a tool will find a defect. A tool with 100% recall can find all defects and is said to be sound. Precision is a measure of a tool’s ability to exclude false positives, defined as the probability that a result corresponds to a real defect. Precision is easy to measure once warning reports have been triaged but it is very difficult to measure recall accurately because the number of false negatives is unknown. Measuring it requires knowing exactly how many defects there really are in the code under analysis. It is important to point out that precision and recall can vary enormously among defect classes, even for a single tool. A tool that is very good at finding buffer overruns may not necessarily be very good at finding resource leaks

Why False Positives and Negatives are Inevitable

For the majority of users, the real measure of the usefulness of a static analysis tool is whether it can find a reasonable number of important bugs in their programs without overwhelming them with useless reports, all without using an unreasonable amount of computing resources. The rub is that the very same properties that make these tools practical to use also mean that they are vulnerable to false positives and false negatives.

Static analysis tools work by creating a model of the code to be analyzed, and then querying that model in various ways. The model usually consists of a set of intermediate representations such as symbol tables, abstract syntax trees, control flow graphs, the program call graph, and so forth. The querying mechanisms can range from simple searches of those data structures through very sophisticated algorithms based on advanced concepts such as dataflow analysis, symbolic execution, abstract interpretation or model checking. The important thing to emphasize is that the analysis operates not directly on the program, but on a model of the program, and models are almost never exact — they are either over- or under-approximations. If a model is exact or an over-approximation, then it is said to be sound because it captures all possible behaviors of the actual program. A sound model with a sound analysis yield results with no false negatives. If a model is an under-approximation then it fails to capture some relevant aspect of the program and false positive results can be generated, regardless of whether the analysis is sound or not.

To understand why it is practically impossible to create an exact perfect fidelity model of a program, it is worth describing how a tool might do so. If the tool is operating on source code it will first have to parse the code in exactly the same way as it is parsed by the compiler used to create the object code. Then the analyzer must have precisely the same interpretation of the language semantics as the compiler so that it can create a model that accurately reflects what will happen when the program executes. Language specifications are riddled with problems that make this difficult, like the new C++11 standard, for example. Also, the same code can have subtly different behavior depending on the platform for which it is compiled.

Because of these difficulties, all general-purpose practical static analysis tools create program models that are over-approximations in some senses and under-approximations in others, so even if the analysis algorithms were perfect, false positives and false negatives would inevitably slip in. Of course the analysis algorithms are not perfect either. They make their own approximations. The most compelling motivation for this is so that they will scale to large programs. The market demands tools that complete in a small multiple of the time to do a regular build. Unfortunately, many of the algorithms are fundamentally super linear if they are to be precise. For example, consider an analysis that is path sensitive: capable of computing information about individual paths through the program. The number of paths through a single procedure with no calls or loops is exponential in the number of conditionals. Clearly no algorithm can hope to be approximately linear if it tries to enumerate all possible paths separately. Instead, tools reason about paths in the aggregate, and deploy other strategies to keep the analysis close to linear.

When True is False

So far I have been using the traditional technical definitions for true and false positive — a true positive is a correct report about a real defect, and a false positive is a report about a bug that does not really exist. However, this is almost never the exact criterion that end users employ when looking at reports. What really matters to an end user is whether the report yields useful and actionable information. There is a great deal of variation in how to interpret results depending on the nature of the defect, the role of the user, the platform on which the application will run, and the environment in which it is deployed. In “Mistakes That Matter” Bill Pugh describes his experience with this issue when deploying static analysis at Google. Take for example a true positive report of a buffer overrun, one of the most notorious classes of C/C++ defects from a security perspective. In the early stages of application development it almost always makes sense to change the code to fix such a bug. The programmers are actively changing the code anyway so fixing it involves little extra overhead. However if the same defect is found after the application has been deployed then it is much trickier to decide whether it is worth fixing. It might be a benign buffer overrun that overwrites a single byte of otherwise unused memory, so impossible for a malicious attacker to exploit. In such a case it might be very expensive to fix the code, retest the application, and redeploy it. This expense, coupled with the risk that any change to the code may introduce a new defect, may mean that it is just not worthwhile to correct the defect at that time. Another example is a security analyst will typically consider a redundant condition warning (i.e., a condition whose value is always true or always false) unhelpful, but the person charged with testing that code to achieve 100% condition coverage would be happy to get such a report because it means that they don’t have to waste time trying to generate data to test the impossible.

Counter intuitively, there are even situations where it makes sense to change the code in response to a false positive report. Many programmers react strongly to this; after all, the tool is clearly wrong about the program, so why would they need to change the code? However, by changing the code, the programmer is making it easier for the analysis to produce useful results, thereby increasing the chance of it finding real defects. Changing the code leverages the power of automation. Such changes also make the code easier for a human to understand. This principle is a core tenet of Holzmann’s “Power of 10” rules for safety critical programming.

The key point is that software development is an economic activity — the challenge is to make the most effective use of the resources available. The cost to fix a real bug may exceed the benefit of fixing it, and the benefit of “correcting” a false positive may exceed the cost of leaving it alone. Tools don’t have good ways of judging such things, and can only be relied on to give narrow technical answers. It is proper that humans be the judge of which static analysis results should be acted upon. Of course humans are not perfect judges either, and there are some pitfalls associated with interpreting static analysis results as I’ll explain in the next section.

Human Factors

Static analysis tools are designed to produce reports that are subsequently then get triaged by a human. However, we humans make mistakes. We have innate cognitive biases and a limited attention span. We may misjudge some reports, and we may introduce new errors as we fix old ones. In order to understand how to make the best use of a static-analysis tool, it is essential to take these frailties into account. Naively, it would appear that the most effective tool is the one that finds the most real bugs, i.e. the one with the highest recall. However, even a tool with perfect recall can be worse than useless if it also has poor precision. Too many false positives can drown out the true positives, which wastes time and makes it very difficult for a human to tell them apart. It takes some care to distinguish a true positive from a false positive, and if a user is accustomed to looking at false positives all day, a fatigue sets in that makes it more difficult to find the real bugs.

There are ways to efficiently process and dismiss of false positives in bulk, assuming that they are easy to recognize, and many tools can be configured to do so automatically. It is also possible to reduce the human workload by automatically prioritizing warnings based on risk. However, it remains true that once this is done, the remaining warnings will still consist of some true and some false positives, and that it requires human judgment to tell them apart.

Users dislike false positives, often intensely. This strong emotional reaction has a disproportionate effect on the way tools are designed, configured and used. If given a choice between a configuration that reports 40 real defects and 10 false positives, and a configuration that reports 50 real bugs but with 50 false positives, our experience is that users will almost always prefer the former, even though it is finding fewer real defects. This is perfectly understandable — users are being asked to weigh an immediate concrete negative (time wasted looking at false positives) against an intangible potential future positive (bugs that may not show up). This is not to say that the users are necessarily wrong to do so. Perhaps the expense spent poring over those extra 40 false positives exceeds the benefit of finding and fixing those 10 extra defects.

In my next post, I'll consider the economics of static analysis tool usage in terms of the relationship between precision, recall rate and number of real defects found. This provides a way to objectively compare tools based on the costs of reviewing error reports and the opportunity costs of missed defects. 


The real measure of the usefulness of a static analysis tool is whether it can find a reasonable number of important bugs in their programs without overwhelming them with useless reports, all without using an unreasonable amount of computing resources. However, even a tool with perfect recall can be worse if it also has poor precision. Too many false positives can drown out the true positives and missed true defects have significant costs if left undetected into final products. Evaluation of static analysis tools based on their ability to balance precision and handling of false positives is needed.