Thursday 29 October 2015

Error guessing in software testing

Error guessing in software testing

·          The Error guessing is a technique where the experienced and good testers are encouraged to think of situations in which the software may not be able to cope. Some people seem to be naturally good at testing and others are good testers because they have a lot of experience either as a tester or working with a particular system and so are able to find out its weaknesses. This is why an error guessing approach, used after more formal techniques have been applied to some extent, can be very effective. It also saves a lot of time because of the assumptions and guessing made by the experienced testers to find out the defects which otherwise won’t be able to find.
·          The success of error guessing is very much dependent on the skill of the tester, as good testers know where the defects are most likely to be.
·          This is why an error guessing approach, used after more formal techniques have been applied to some extent, can be very effective. In using more formal techniques, the tester is likely to gain a better understanding of the system, what it does and how it works. With this better understanding, he or she is likely to be better at guessing ways in which the system may not work properly.
·          Typical conditions to try include division by zero, blank (or no) input, empty files and the wrong kind of data (e.g. alphabetic characters where numeric are required). If anyone ever says of a system or the environment in which it is to operate ‘That could never happen’, it might be a good idea to test that condition, as such assumptions about what will and will not happen in the live environment are often the cause of failures.
·          A structured approach to the error-guessing technique is to list possible defects or failures and to design tests that attempt to produce them. These defect and failure lists can be built based on the tester’s own experience or that of other people, available defect and failure data, and from common knowledge about why software fails.

 




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