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at one time) Looking at each line one at a time makes for fast processing, but the lack of context necessarily means that the analysis will be super cial At the other extreme, analyzing an entire program or an entire system provides much better context for the analysis but is expensive in terms of time, memory, or both In between are tools that look at individual functions or modules one at a time From a user s perspective, static analysis tools come in several speed grades The fastest tools provide almost instantaneous feedback These tools could be built into an IDE the same way an interactive spell checker is built into Microsoft Word, or they could run every time the compiler runs With the next rung up, users might be willing to take a coffee break or get lunch while the tool runs A programmer might use such a tool once a day or just before committing code to the source repository At the top end, tools give up any pretense at being interactive and run overnight or over a weekend Such tools are best suited to run as part of a nightly build or a milestone build Naturally, the greater the depth of the analysis, the greater the runtime of the tool To give a rough sense of the trade-offs that tools make, Figure 22 considers the bug nding and security tools discussed earlier in the chapter and plots their execution time versus the scope of the analysis they perform
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Figure 22 Analysis scope vs execution time for the bug nding and security tools discussed in Section 21
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2 Introduction to Static Analysis
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Finding the Right Stuff Static analysis tools must be armed with the right set of defects to search for What the right set consists of depends entirely upon the purpose of the software being analyzed Clients fail differently than servers Operating systems fail differently than desktop applications The makers of a static analysis tool must somehow take this context into account With research tools, the most common approach is to build a tool that targets only a small number of scenarios Commercial tools sometimes ask the user to select the scenario at hand to make decisions about what to report Even with a limited purview, the most valuable things to search for are often speci c to the particular piece of software being evaluated Finding these defects requires the tool to be extensible; users must be able to add their own custom rules For example, detecting locations where private data are made public or otherwise mismanaged by a program requires adding custom rules that tell the analysis tool which pieces of data are considered private Just as a good program model requires a thorough characterization of the behavior of libraries and system interfaces, detecting defects requires a thorough set of rules that de ne where and under what circumstances the defects can occur The size of the rule set is the rst and most obvious means of comparing the capabilities of static analysis tools [McGraw, 2006], but counting the number of rules that a tool has does not tell the whole story, especially if a single rule can be applied in a variety of circumstances or can contain wildcards that match against entire families of functions Comparing static analysis tools based on the size of their rule sets is like comparing operating systems based on the number of lines of source code they are built from The best way to compare static analysis tools is by using them to analyze the same code and comparing the results, but choosing the right code for comparing tools is no small problem in and of itself A number of attempts at creating static analysis benchmarks have arisen in the last few years: Benjamin Livshits has put together two benchmarks for static analysis tools SecuriBench (http://suifstanfordedu/~livshits/securibench/) is a collection of open source Web-based Java programs that contain known security defects SecuriBench Micro (http://suifstanfordedu/~livshits/ work/securibench-micro/) is a set of small hand-crafted Java programs that are intentionally written to stress different aspects of a static analysis tool Zitser, Lippman, and Leek have assembled a small collection of vulnerable programs derived from real-world vulnerable programs for the
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