Data Verification (DVR) Class

Definition

We define Data Verification Bugs (DVR) as follows:

Data are verified (semantics check) or corrected (assign value, remove) improperly.

Taxonomy

Fig. 1 depicts DVR causes, attributes and consequences.

Operations

The DVR operations are: Verify and Correct. They reflect the improper check and modification of data semantics.

Operation Value	Definition
Verify	Check data semantics (proper value/meaning) in order to accept (and possibly correct) or reject it.
Correct	Modify data (assign new value, remove), in order to make it accurate.

Causes

The graph of causes shows that there are two main causes for data verification bugs: Improper Operation and Improper Data.

Improper Operation
Value	Definition	Example
Missing	The operation is absent.	Missing data sanitization.
Erroneous	There is a bug in the implementation of the operation (incl. how it checks against a policy).	Using not equal to (!=) when comparing to safelist values. Using greater than (>) when checking for upper range.
Under-Restrictive Policy	Accepts bad data.	Permissive safelist or regular expression. Incomplete denylist.
Over-Restrictive Policy	Rejects good data.	Over-restrictive denylist or regular expression.

Improper Data
Value	Definition
Invalid Data	Data with harmed syntax due to sanitization errors.

Consequences

The graph of consequences shows Improper Data for Next Operation as a consequence.

Improper Data for Next Operation
Value	Definition
Invalid Data	Data with harmed syntax due to sanitization errors.
Wrong Value	Data with harmed semantics; not accurate value (e.g., outside of a range).
Inconsistent Value	Data value is inconsistent with the value of a related data (e.g., inconstancy between the value of a size variable and the actual buffer size).
Wrong Type	Data with wrong data type.

Attributes

The attributes of DVR are:

Name	Value	Definition
Mechanism	Value	Check data for a specific value (incl. NULL or list of values).
	Quantity	Check data for a specific measurable value (e.g., size, time, rate, frequency).
	Range	Check data are within a (min, max) interval.
	Type	Check data type (e.g., int, float, XSD-XML Schema Definition, or specific structure/object).
	Other Rules	Check data against other business logic.
Source Code	Codebase	The operation is in programmer’s code – in the application itself.
	Third Party	The operation is in a third party library.
	Standard Library	The operation is in the standard library for a particular programming language.
	Language Processor	The operation is in the tool that allows execution or creates executable (compiler, assembler, interpreter).
Execution Space	Local	The bugged code runs in an environment with access control policy with limited (local user) permission.
	Admin	The bugged code runs in an environment with access control policy with unlimited (admin user) permission.
	Bare-Metal	The bugged code runs in an environment with-out privilege control. Usually, the program is the only software running and has total access to the hardware.
State	Entered	Data comes from user interface (e.g., text field).
	Stored	Data comes from permanent storage (e.g., file, database on a storage device).
	In Use	Data comes from volatile storage (e.g., RAM, cache memory).
	Transferred	Data comes via network (e.g., connecting analog device or another computer).

Sites

A site for input/output check bugs is any part of the code that should check and sanitize data syntax or check and correct data semantics.

Application

Application examples are provided here.