In decision table testing, what does each condition in the table represent?
Think about what influences the decisions in the system.
Each condition in a decision table represents an input or state that influences how the system behaves. These conditions combine to define different scenarios.
Given a decision table with 3 conditions each having 2 possible values (True/False), how many unique test cases does the table represent?
Calculate the total combinations of all conditions.
Each condition has 2 values, so total test cases = 2 * 2 * 2 = 8.
Which assertion correctly verifies that all decision table rules have been tested?
tested_rules = {1, 2, 3, 4}
all_rules = {1, 2, 3, 4}
# Which assertion below is correct?Think about what it means to have tested all rules.
To confirm all rules are tested, the sets must be exactly equal.
What error will this Python code raise when checking decision table rules coverage?
all_rules = {1, 2, 3, 4}
tested_rules = [1, 2, 3, 4]
assert tested_rules == all_rulesCheck the types of variables compared.
Comparing a list and a set with == returns False but does not raise an error. However, the assertion fails causing AssertionError, not TypeError. So check carefully.
Which approach best automates testing all rules in a decision table using a test framework?
Think about maintainability and coverage.
Using a loop over all rules in one test function ensures all rules are tested systematically and code is easy to maintain.