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  2. Bounds checking - Wikipedia

    en.wikipedia.org/wiki/Bounds_checking

    In computer programming, bounds checking is any method of detecting whether a variable is within some bounds before it is used. It is usually used to ensure that a number fits into a given type (range checking), or that a variable being used as an array index is within the bounds of the array (index checking).

  3. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  4. Data validation - Wikipedia

    en.wikipedia.org/wiki/Data_validation

    Checks that the data is in a specified format (template), e.g., dates have to be in the format YYYY-MM-DD. Regular expressions may be used for this kind of validation. Presence check Checks that data is present, e.g., customers may be required to have an email address. Range check

  5. Bounds-checking elimination - Wikipedia

    en.wikipedia.org/wiki/Bounds-checking_elimination

    In computer science, bounds-checking elimination is a compiler optimization useful in programming languages or runtime systems that enforce bounds checking, the practice of checking every index into an array to verify that the index is within the defined valid range of indexes. [1]

  6. Checksum - Wikipedia

    en.wikipedia.org/wiki/Checksum

    The simplest checksum algorithm is the so-called longitudinal parity check, which breaks the data into "words" with a fixed number n of bits, and then computes the bitwise exclusive or (XOR) of all those words. The result is appended to the message as an extra word.

  7. Data cleansing - Wikipedia

    en.wikipedia.org/wiki/Data_cleansing

    Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.

  8. List of tools for static code analysis - Wikipedia

    en.wikipedia.org/wiki/List_of_tools_for_static...

    PyCharm – Cross-platform Python IDE with code inspections available for analyzing code on-the-fly in the editor and bulk analysis of the whole project. PyDev – Eclipse-based Python IDE with code analysis available on-the-fly in the editor or at save time. Pylint – Static code analyzer. Quite stringent; includes many stylistic warnings as ...

  9. Verification and validation - Wikipedia

    en.wikipedia.org/wiki/Verification_and_validation

    Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.