Sanity check explained

A sanity check or sanity test is a basic test to quickly evaluate whether a claim or the result of a calculation can possibly be true. It is a simple check to see if the produced material is rational (that the material's creator was thinking rationally, applying sanity). The point of a sanity test is to rule out certain classes of obviously false results, not to catch every possible error. A rule-of-thumb or back-of-the-envelope calculation may be checked to perform the test. The advantage of performing an initial sanity test is that of speedily evaluating basic function.

In arithmetic, for example, when multiplying by 9, using the divisibility rule for 9 to verify that the sum of digits of the result is divisible by 9 is a sanity test—it will not catch every multiplication error, but is a quick and simple method to discover many possible errors.

In computer science, a sanity test is a very brief run-through of the functionality of a computer program, system, calculation, or other analysis, to assure that part of the system or methodology works roughly as expected. This is often prior to a more exhaustive round of testing.

Use in different fields

Mathematical

A sanity test can refer to various orders of magnitude and other simple rule-of-thumb devices applied to cross-check mathematical calculations. For example:

Physical

Software development

See also: Smoke testing (software). In software development, a sanity test (a form of software testing which offers "quick, broad, and shallow testing"[1]) evaluates the result of a subset of application functionality to determine whether it is possible and reasonable to proceed with further testing of the entire application.[2] Sanity tests may sometimes be used interchangeably with smoke tests[3] insofar as both terms denote tests which determine whether it is possible and reasonable to continue testing further. On the other hand, a distinction is sometimes made that a smoke test is a non-exhaustive test that ascertains whether the most crucial functions of a programme work before proceeding with further testing whereas a sanity test refers to whether specific functionality such as a particular bug fix works as expected without testing the wider functionality of the software. In other words, a sanity test determines whether the intended result of a code change works correctly while a smoke test ensures that nothing else important was broken in the process. Sanity testing and smoke testing avoid wasting time and effort by quickly determining whether an application is too flawed to merit more rigorous QA testing, but needs more developer debugging.

Groups of sanity tests are often bundled together for automated unit testing of functions, libraries, or applications prior to merging development code into a testing or trunk version control branch,[4] for automated building,[5] or for continuous integration and continuous deployment.[6]

Another common usage of sanity test is to denote checks which are performed programme code, usually on arguments to functions or returns therefrom, to see if the answers can be assumed to be correct. The more complicated the routine, the more important that its response be checked. The trivial case is checking to see whether the return value of a function indicated success or failure, and to therefore cease further processing upon failure. This return value is actually often itself the result of a sanity check. For example, if the function attempted to open, write to, and close a file, a sanity check may be used to ensure that it did not fail on any of these actions—which is a sanity check often ignored by programmers.[7]

These kinds of sanity checks may be used during development for debugging purposes and also to aid in troubleshooting software runtime errors. For example, in a bank account management application, a sanity check will fail if a withdrawal requests more money than the total account balance rather than allowing the account to go negative (which wouldn't be sane). Another sanity test might be that deposits or purchases correspond to patterns established by historical data—for example, large purchase transactions or ATM withdrawals in foreign locations never before visited by the cardholder may be flagged for confirmation.

Sanity checks are also performed upon installation of stable, production software code into a new computing environment to ensure that all dependencies are met, such as a compatible operating system and link libraries. When a computing environment has passed all the sanity checks, it's known as a sane environment for the installation programme to proceed with reasonable expectation of success.

A "Hello, World!" program is often used as a sanity test for a development environment similarly. Rather than a complicated script running a set of unit tests, if this simple programme fails to compile or execute, it proves that the supporting environment likely has a configuration problem that will prevent any code from compiling or executing. But if "Hello world" executes, then any problems experienced with other programmes likely can be attributed to errors in that application's code rather than the environment.

The Association for Computing Machinery,[8] and software projects such as Android,[9] MediaWiki[10] and Twitter,[11] discourage use of the phrase sanity check in favour of other terms such as confidence test, coherence check, or simply test, as part of a wider attempt to avoid ableist language and increase inclusivity.

See also

Notes and References

  1. Fecko . Mariusz A. . Lott . Christopher M. . October 2002 . Lessons learned from automating tests for an operations support system . Software: Practice and Experience . 32 . 15 . 1485–1506 . 10.1002/spe.491 . 16820529 . https://web.archive.org/web/20030717041855/http://www.chris-lott.org/work/pubs/2002-spe.pdf . 17 July 2003.
  2. Sammi . Rabia . Masood . Iram . Jabeen . Shunaila . 2011 . Zain . Jasni Mohamad . Wan Mohd . Wan Maseri bt . El-Qawasmeh . Eyas . A Framework to Assure the Quality of Sanity Check Process . Software Engineering and Computer Systems . Communications in Computer and Information Science . Berlin, Heidelberg . Springer . 181 . 143–150 . 10.1007/978-3-642-22203-0_13 . 978-3-642-22203-0.
  3. ISTQB® Glossary for the International Software Testing Qualification Board® software testing qualification scheme, ISTQB Glossary International Software Testing Qualification Board
  4. Jørgensen . Niels . 10.1046/J.1365-2575.2001.00113.X . 4 . Information Systems Journal . 321 . Putting it all in the trunk: incremental software development in the FreeBSD open source project . 11 . 2001.
  5. Hassan, A. E. and Zhang, K. 2006. Using Decision Trees to Predict the Certification Result of a Build. In Proceedings of the 21st IEEE/ACM international Conference on Automated Software Engineering (September 18 – 22, 2006). Automated Software Engineering. IEEE Computer Society, Washington, DC, 189–198.
  6. Chasidim . Hadas . Almog . Dani . Sohacheski . Dov Benyomin . Gillenson . Mark Lee . Poston . Robin S. . Mark . Shlomo . 2 . Journal of Information Technology Management . 40–54 . The Unit Test: Facing CICD - Are They Elusive Definitions? . 29 . 2018.
  7. Book: Darwin, Ian F. . Checking C programs with lint . January 1991 . O'Reilly & Associates . 0-937175-30-7 . 1st ed., with minor revisions. . Newton, Mass. . 19 . A common programming habit is to ignore the return value from fprintf(stderr, ... . 7 October 2014.
  8. Web site: 2020-11-20. 2023-06-29. Words Matter.
  9. Web site: 2022-11-16 . Coding with respect . 2023-01-23 . Android Open Source Project . en.
  10. Web site: Inclusive language/en-gb - MediaWiki . 2023-01-23 . www.mediawiki.org . en.
  11. Web site: Twitter Engineering. 2023-01-23 . Twitter . en-GB.