The sophistication of the analysis performed by tools varies from those that only consider the behaviour of individual statements and declarations, to those that include the complete source code of a program in their analysis. The uses of the information obtained from the analysis vary from highlighting possible coding errors (e.g., the lint tool) to formal methods that mathematically prove properties about a given program (e.g., its behaviour matches that of its specification).
Software metrics and reverse engineering can be described as forms of static analysis. Deriving software metrics and static analysis are increasingly deployed together, especially in creation of embedded systems, by defining so-called software quality objectives.
A growing commercial use of static analysis is in the verification of properties of software used in safety-critical computer systems and
locating potentially vulnerable code. For example, the following industries have identified the use of static code analysis as a means of improving the quality of increasingly sophisticated and complex software:
A study in 2012 by VDC Research reports that 28.7% of the embedded software engineers surveyed currently use static analysis tools and 39.7% expect to use them within 2 years.
A study from 2010 found that 60% of the interviewed developers in European research projects made at least use of their basic IDE built-in static analyzers. However, only about 10% employed an additional other (and perhaps more advanced) analysis tool.
In the application security industry the name Static Application Security Testing (SAST) is also used. SAST is an important part of Security Development Lifecycles (SDLs) such as the SDL defined by Microsoft  and a common practice in software companies.
The OMG (Object Management Group) published a study regarding the types of software analysis required for software quality measurement and assessment. This document on "How to Deliver Resilient, Secure, Efficient, and Easily Changed IT Systems in Line with CISQ Recommendations" describes three levels of software analysis.
Analysis that takes place within a specific program or subroutine, without connecting to the context of that program.
Analysis that takes into account interactions between unit programs to get a more holistic and semantic view of the overall program in order to find issues and avoid obvious false positives. For instance, it is possible to statically analyze the Android technology stack to find permission errors.
Analysis that takes into account the interactions between unit programs, but without being limited to one specific technology or programming language.
A further level of software analysis can be defined.
Analysis that takes into account the business/mission layer terms, rules and processes that are implemented within the software system for its operation as part of enterprise or program/mission layer activities. These elements are implemented without being limited to one specific technology or programming language and in many cases are distributed across multiple languages, but are statically extracted and analyzed for system understanding for mission assurance.
By a straightforward reduction to the halting problem, it is possible to prove that (for any Turing complete language), finding all possible run-time errors in an arbitrary program (or more generally any kind of violation of a specification on the final result of a program) is undecidable: there is no mechanical method that can always answer truthfully whether an arbitrary program may or may not exhibit runtime errors. This result dates from the works of Church, Gödel and Turing in the 1930s (see: Halting problem and Rice's theorem). As with many undecidable questions, one can still attempt to give useful approximate solutions.
Some of the implementation techniques of formal static analysis include:
Abstract interpretation, to model the effect that every statement has on the state of an abstract machine (i.e., it 'executes' the software based on the mathematical properties of each statement and declaration). This abstract machine over-approximates the behaviours of the system: the abstract system is thus made simpler to analyze, at the expense of incompleteness (not every property true of the original system is true of the abstract system). If properly done, though, abstract interpretation is sound (every property true of the abstract system can be mapped to a true property of the original system). The Frama-C value analysis plugin and Polyspace heavily rely on abstract interpretation.
Data-flow analysis, a lattice-based technique for gathering information about the possible set of values;
Symbolic execution, as used to derive mathematical expressions representing the value of mutated variables at particular points in the code.
Data-driven static analysis
Data-driven static analysis uses large amounts of code to infer coding rules. For instance, one can use all Java open-source packages on GitHub to learn a good analysis strategy. The rule inference can use machine learning techniques. For instance, it has been shown that when one deviates too much in the way one uses an object-oriented API, it is likely to be a bug. It is also possible to learn from a large amount of past fixes and warnings.
^"Software Quality Objectives for Source Code"Archived 2015-06-04 at the Wayback Machine (PDF). Proceedings: Embedded Real Time Software and Systems 2010 Conference, ERTS2010.org, Toulouse, France: Patrick Briand, Martin Brochet, Thierry Cambois, Emmanuel Coutenceau, Olivier Guetta, Daniel Mainberte, Frederic Mondot, Patrick Munier, Loic Noury, Philippe Spozio, Frederic Retailleau.
^Computer based safety systems - technical guidance for assessing software aspects of digital computer based protection systems, "Computer based safety systems"(PDF). Archived from the original(PDF) on January 4, 2013. Retrieved May 15, 2013.
^Oh, Hakjoo; Yang, Hongseok; Yi, Kwangkeun (2015). "Learning a strategy for adapting a program analysis via bayesian optimisation". Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications - OOPSLA 2015. pp. 572–588. doi:10.1145/2814270.2814309. ISBN9781450336895.