Modified Condition/Decision Coverage

Detailed explanation

Modified Condition/Decision Coverage (MC/DC) is a rigorous white-box testing technique used to ensure that each condition within a decision statement independently affects the outcome of that decision. It's a stronger form of coverage than branch coverage or decision coverage, as it focuses on isolating the impact of individual conditions. MC/DC is particularly important in safety-critical systems, such as those found in aerospace, automotive, and medical devices, where failures can have severe consequences.

The core principle of MC/DC is to demonstrate that each condition in a decision can independently influence the final outcome. This involves creating test cases that systematically vary each condition while keeping all other conditions constant. By doing so, you can isolate the effect of each condition and verify that it behaves as expected.

To achieve MC/DC, you need to satisfy the following criteria for each condition in a decision:

1. Every point of entry and exit in the program has been invoked at least once. This is basic statement coverage.
2. Every decision in the program has taken all possible outcomes at least once. This is decision coverage.
3. Each condition in a decision has taken all possible outcomes at least once. This is condition coverage.
4. Each condition in a decision has been shown to independently affect the decision's outcome. This is the "modified" part of MC/DC.

Let's illustrate MC/DC with a simple example in Python:

def process_data(a, b, c):
    if (a > 0) and (b < 10) or (c == 5):
        result = "Condition met"
    else:
        result = "Condition not met"
    return result

In this example, the decision is (a > 0) and (b < 10) or (c == 5). We have three conditions:

• Condition 1: a > 0
• Condition 2: b < 10
• Condition 3: c == 5

To achieve MC/DC, we need to create test cases that demonstrate the independent effect of each condition. Here's a possible set of test cases:

Let's analyze how these test cases satisfy MC/DC:

• Condition 1 (a > 0): Test cases 1 and 2 show that changing a from 1 to -1 (while keeping b and c constant) changes the decision outcome from "Condition met" to "Condition not met". This demonstrates the independent effect of condition 1.
• Condition 2 (b < 10): Test cases 1 and 3 show that changing b from 5 to 15 (while keeping a and c constant) changes the decision outcome from "Condition met" to "Condition not met". This demonstrates the independent effect of condition 2.
• Condition 3 (c == 5): Test cases 3 and 5 show that changing c from 0 to 5 (while keeping a and b constant) changes the decision outcome from "Condition not met" to "Condition met". This demonstrates the independent effect of condition 3.

Therefore, this set of test cases achieves MC/DC for the given code.

Practical Implementation and Best Practices

Implementing MC/DC can be challenging, especially for complex decision statements. Here are some best practices:

• Start with a clear understanding of the requirements: Before writing test cases, ensure you thoroughly understand the logic of the code and the intended behavior of each condition.
• Use a truth table: Create a truth table to visualize all possible combinations of conditions and their corresponding decision outcomes. This can help you identify the test cases needed to satisfy MC/DC.
• Automate test case generation: For complex systems, consider using automated test case generation tools that can help you create test cases that satisfy MC/DC.
• Document your test cases: Clearly document the purpose of each test case and how it demonstrates the independent effect of a specific condition.
• Use code coverage tools: Employ code coverage tools to measure the level of MC/DC achieved by your test suite. These tools can help you identify gaps in your coverage and guide you in creating additional test cases.
• Consider pairwise testing: In situations where achieving full MC/DC is impractical due to the complexity of the decision statements, consider using pairwise testing to cover all pairs of conditions. While not as rigorous as MC/DC, pairwise testing can still provide a good level of coverage.

Common Tools

Several tools can assist with MC/DC testing:

• Code Coverage Analyzers: Tools like JaCoCo (for Java), gcov/lcov (for C/C++), and coverage.py (for Python) can measure code coverage, including branch coverage and condition coverage, which are prerequisites for MC/DC. While they don't directly measure MC/DC, they help identify areas where coverage is lacking.
• Test Case Generation Tools: Tools like Conformiq and Tessy can automatically generate test cases that satisfy MC/DC. These tools are particularly useful for complex systems with many conditions.
• LDRA Testbed: LDRA is a suite of tools specifically designed for safety-critical software development. It provides comprehensive support for MC/DC testing, including test case generation, code coverage analysis, and requirements traceability.

MC/DC is a powerful technique for ensuring the reliability and safety of software systems. By systematically testing the independent effect of each condition in a decision, you can significantly reduce the risk of errors and improve the overall quality of your code. While it can be challenging to implement, the benefits of MC/DC in terms of increased confidence and reduced risk make it a worthwhile investment, especially for safety-critical applications.

Further reading

• Wikipedia: Modified condition/decision coverage
• VectorCAST MC/DC Coverage
• MathWorks: Modified Condition/Decision Coverage (MC/DC) Testing