Code Coverage Analysis

Contents

Code coverage analysis is the process of:

• Finding areas of a program not exercised by a set of test cases,
• Creating additional test cases to increase coverage, and
• Determining a quantitative measure of code coverage, which is an indirect measure of quality.

An optional aspect of code coverage analysis is:

• Identifying redundant test cases that do not increase coverage.

A code coverage analyzer automates this process.

You use coverage analysis to assure quality of your set of tests, not the quality of the actual product. You do not generally use a coverage analyzer when running your set of tests through your release candidate. Coverage analysis requires access to test program source code and often requires recompiling it with a special command.

This paper discusses the details you should consider when planning to add coverage analysis to your test plan. Coverage analysis has certain strengths and weaknesses. You must choose from a range of measurement methods. You should establish a minimum percentage of coverage, to determine when to stop analyzing coverage. Coverage analysis is one of many testing techniques; you should not rely on it alone.

Code coverage analysis is sometimes called test coverage analysis. The two terms are synonymous. The academic world more often uses the term "test coverage" while practitioners more often use "code coverage". Likewise, a coverage analyzer is sometimes called a coverage monitor. I prefer the practitioner terms.

Code coverage analysis is a structural testing technique (AKA glass box testing and white box testing). Structural testing compares test program behavior against the apparent intention of the source code. This contrasts with functional testing (AKA black-box testing), which compares test program behavior against a requirements specification. Structural testing examines how the program works, taking into account possible pitfalls in the structure and logic. Functional testing examines what the program accomplishes, without regard to how it works internally.

Structural testing is also called path testing since you choose test cases that cause paths to be taken through the structure of the program. Do not confuse path testing with the metric, explained later.

At first glance, structural testing seems unsafe. Structural testing cannot find errors of omission. However, requirements specifications sometimes do not exist, and are rarely complete. This is especially true near the end of the product development time line when the requirements specification is updated less frequently and the product itself begins to take over the role of the specification. The difference between functional and structural testing blurs near release time.

The basic assumptions behind coverage analysis tell us about the strengths and limitations of this testing technique. Some fundamental assumptions are listed below.

• Bugs relate to control flow and you can expose Bugs by varying the control flow [ p.60]. For example, a programmer wrote "if (c)" rather than "if (!c)".
• You can look for failures without knowing what failures might occur and all tests are reliable, in that successful test runs imply program correctness []. The tester understands what a correct version of the program would do and can identify differences from the correct behavior.
• Other assumptions include achievable specifications, no errors of omission, and no unreachable code.

Clearly, these assumptions do not always hold. Coverage analysis exposes some plausible bugs but does not come close to exposing all classes of bugs. Coverage analysis provides more benefit when applied to an application that makes a lot of decisions rather than data-centric applications, such as a database application.

A large variety of coverage metrics exist. Here is a description of some fundamental metrics and their strengths and weaknesses.

This metric reports whether each executable statement is encountered.

Also known as: line coverage, segment coverage [], C1 [ p.75] and basic block coverage. Basic block coverage is the same as statement coverage except the unit of code measured is each sequence of non-branching statements.

I highly discourage using the undescriptive name C1. People sometimes incorrectly use the name C1 to identify . Therefore this term has become ambiguous.

The chief advantage of this metric is that it can be applied directly to object code and does not require processing source code. Performance profilers commonly implement this metric.

The chief disadvantage of statement coverage is that it is insensitive to some control structures. For example, consider the following C/C++ code fragment:

int* p = NULL;
if (condition)
    p = &variable;
*p = 123;

Without a test case that causes condition to evaluate false, statement coverage rates this code fully covered. In fact, if condition ever evaluates false, this code fails. This is the most serious shortcoming of statement coverage. If-statements are very common.

Statement coverage does not report whether loops reach their termination condition - only whether the loop body was executed. With C, C++, and Java, this limitation affects loops that contain break statements.

Since do-while loops always execute at least once, statement coverage considers them the same rank as non-branching statements.

Statement coverage is completely insensitive to the logical operators (|| and &&).

Statement coverage cannot distinguish consecutive switch labels.

Test cases generally correlate more to decisions than to statements. You probably would not have 10 separate test cases for a sequence of 10 non-branching statements; you would have only one test case. For example, consider an if-else statement containing one statement in the then-clause and 99 statements in the else-clause. After exercising one of the two possible paths, statement coverage gives extreme results: either 1% or 99% coverage. Basic block coverage eliminates this problem.

One argument in favor of statement coverage over other metrics is that bugs are evenly distributed through code; therefore the percentage of executable statements covered reflects the percentage of faults discovered. However, one of our fundamental assumptions is that faults are related to control flow, not computations. Additionally, we could reasonably expect that programmers strive for a relatively constant ratio of branches to statements.

In summary, this metric is affected more by computational statements than by decisions.

This metric reports whether boolean expressions tested in control structures (such as the if-statement and while-statement) evaluated to both true and false. The entire boolean expression is considered one true-or-false predicate regardless of whether it contains logical-and or logical-or operators. Additionally, this metric includes coverage of switch-statement cases, exception handlers, and interrupt handlers.

Also known as: branch coverage, all-edges coverage [ p.58], basis path coverage [ p.48], C2 [ p.75], decision-decision-path testing [ p.39]. "Basis path" testing selects paths that achieve decision coverage.

I discourage using the undescriptive name C2 because of the confusion with the term C1.

This metric has the advantage of simplicity without the problems of .

A disadvantage is that this metric ignores branches within boolean expressions which occur due to short-circuit operators. For example, consider the following C/C++/Java code fragment:

if (condition1 && (condition2 || function1()))
    statement1;
else
    statement2;

This metric could consider the control structure completely exercised without a call to function1. The test expression is true when condition1 is true and condition2 is true, and the test expression is false when condition1 is false. In this instance, the short-circuit operators preclude a call to function1.

Condition coverage reports the true or false outcome of each boolean sub-expression, separated by logical-and and logical-or if they occur. Condition coverage measures the sub-expressions independently of each other.

This metric is similar to but has better sensitivity to the control flow.

However, full condition coverage does not guarantee full . For example, consider the following C++/Java fragment.

bool f(bool e) { return false; }
bool a[2] = { false, false };
if (f(a && b)) ...
if (a[int(a && b)]) ...
if ((a && b) ? false : false) ...

All three of the if-statements above branch false regardless of the values of a and b. However if you exercise this code with a and b having all possible combinations of values, condition coverage reports full coverage.

Multiple condition coverage reports whether every possible combination of boolean sub-expressions occurs. As with , the sub-expressions are separated by logical-and and logical-or, when present. The test cases required for full multiple condition coverage of a condition are given by the logical operator truth table for the condition.

For languages with short circuit operators such as C, C++, and Java, an advantage of multiple condition coverage is that it requires very thorough testing. For these languages, multiple condition coverage is very similar to .

A disadvantage of this metric is that it can be tedious to determine the minimum set of test cases required, especially for very complex boolean expressions. An additional disadvantage of this metric is that the number of test cases required could vary substantially among conditions that have similar complexity. For example, consider the following two C/C++/Java conditions.

a && b && (c || (d && e))
((a || b) && (c || d)) && e

To achieve full multiple condition coverage, the first condition requires 6 test cases while the second requires 11. Both conditions have the same number of operands and operators. The test cases are listed below.

   a && b && (c || (d && e))
1. F    -     -     -    -
2. T    F     -     -    -
3. T    T     F     F    -
4. T    T     F     T    F
5. T    T     F     T    T
6. T    T     T     -    -

    ((a || b) && (c || d)) && e
 1.   F    F      -    -      -
 2.   F    T      F    F      -  
 3.   F    T      F    T      F
 4.   F    T      F    T      T
 5.   F    T      T    -      F
 6.   F    T      T    -      T
 7.   T    -      F    F      -
 8.   T    -      F    T      F
 9.   T    -      F    T      T
10.   T    -      T    -      F
11.   T    -      T    -      T

As with , multiple condition coverage does not include .

For languages without short circuit operators such as Visual Basic and Pascal, multiple condition coverage is effectively (described below) for logical expressions, with the same advantages and disadvantages. Consider the following Visual Basic code fragment.

If a And b Then
...

Multiple condition coverage requires four test cases, for each of the combinations of a and b both true and false. As with each additional logical operator doubles the number of test cases required.

Condition/Decision Coverage is a hybrid metric composed by the union of and .

It has the advantage of simplicity but without the shortcomings of its component metrics.

measures condition/decision coverage.

Also known as MC/DC and MCDC.

This metric requires enough test cases to verify every condition can affect the result of its encompassing decision []. This metric was created at and is required for aviation software by .

For C, C++ and Java, this metric requires exactly the same test cases as . Modified condition/decision coverage was designed for languages containing logical operators that do not short-circuit. The short circuit logical operators in C, C++ and Java only evaluate conditions when their result can affect the encompassing decision. The paper that defines this metric [] section "3.3 Extensions for short-circuit operators" says "[use of short-circuit operators] forces relaxation of the requirement that all conditions are held fixed while the condition of interest is varied." The only programming language referenced by this paper is , which has logical operators that do not short circuit as well as those that do.

This metric reports whether each of the possible paths in each function have been followed. A path is a unique sequence of branches from the function entry to the exit.

Also known as predicate coverage. Predicate coverage views paths as possible combinations of logical conditions [ p.98].

Since loops introduce an unbounded number of paths, this metric considers only a limited number of looping possibilities. A large number of variations of this metric exist to cope with loops. Boundary-interior path testing considers two possibilities for loops: zero repetitions and more than zero repetitions []. For do-while loops, the two possibilities are one iteration and more than one iteration.

Path coverage has the advantage of requiring very thorough testing. Path coverage has two severe disadvantages. The first is that the number of paths is exponential to the number of branches. For example, a function containing 10 if-statements has 1024 paths to test. Adding just one more if-statement doubles the count to 2048. The second disadvantage is that many paths are impossible to exercise due to relationships of data. For example, consider the following C/C++ code fragment:

if (success)
    statement1;
statement2;
if (success)
    statement3;

Path coverage considers this fragment to contain 4 paths. In fact, only two are feasible: success=false and success=true.

Researchers have invented many variations of path coverage to deal with the large number of paths. For example, n-length sub-path coverage reports whether you exercised each path of length n branches. Others variations include and .

Here is a description of some variations of the fundamental metrics and some less commonly use metrics.

This metric reports whether you invoked each function or procedure. It is useful during preliminary testing to assure at least some coverage in all areas of the software. Broad, shallow testing finds gross deficiencies in a test suite quickly.

measures function coverage.

This metric reports whether you executed each function call. The hypothesis is that bugs commonly occur in interfaces between modules.

Also known as call pair coverage.

This variation of considers only sub-paths that can easily be represented in the program source code, without requiring a flow graph []. An LCSAJ is a sequence of source code lines executed in sequence. This "linear" sequence can contain decisions as long as the control flow actually continues from one line to the next at run-time. Sub-paths are constructed by concatenating LCSAJs. Researchers refer to the coverage ratio of paths of length n LCSAJs as the test effectiveness ratio (TER) n+2.

The advantage of this metric is that it is more thorough than yet avoids the exponential difficulty of . The disadvantage is that it does not avoid infeasible paths.

This variation of considers only the sub-paths from variable assignments to subsequent references of the variables.

The advantage of this metric is the paths reported have direct relevance to the way the program handles data. One disadvantage is that this metric does not include . Another disadvantage is complexity. Researchers have proposed numerous variations, all of which increase the complexity of this metric. For example, variations distinguish between the use of a variable in a computation versus a use in a decision, and between local and global variables. As with data flow analysis for code optimization, pointers also present problems.

This metric reports whether each machine language conditional branch instruction both took the branch and fell through.

This metric gives results that depend on the compiler rather than on the program structure since compiler code generation and optimization techniques can create object code that bears little similarity to the original source code structure.

Since branches disrupt the instruction pipeline, compilers sometimes avoid generating a branch and instead generate an equivalent sequence of non-branching instructions. Compilers often expand the body of a function inline to save the cost of a function call. If such functions contain branches, the number of machine language branches increases dramatically relative to the original source code.

You are better off testing the original source code since it relates to program requirements better than the object code.

This metric reports whether you executed each loop body zero times, exactly once, and more than once (consecutively). For do-while loops, loop coverage reports whether you executed the body exactly once, and more than once.

The valuable aspect of this metric is determining whether while-loops and for-loops execute more than once, information not reported by other metrics.

As far as I know, only implements this metric.

This metric reports whether multiple threads execute the same code at the same time. It helps detect failure to synchronize access to resources. It is useful for testing multi-threaded programs such as in an operating system.

As far as I know, only implements this metric.

This metric reports whether boundary situations occur with relational operators (<, <=, >, >=). The hypothesis is that boundary test cases find off-by-one mistakes and uses of the wrong relational operators such as < instead of <=. For example, consider the following C/C++ code fragment:

if (a < b)
    statement;

Relational operator coverage reports whether the situation a==b occurs. If a==b occurs and the program behaves correctly, you can assume the relational operator is not suppose to be <=.

As far as I know, only implements this metric.

This metric is similar to but much more general []. It reports whether test cases occur which would expose the use of wrong operators and also wrong operands. It works by reporting coverage of conditions derived by substituting (mutating) the program's expressions with alternate operators, such as "-" substituted for "+", and with alternate variables substituted.

This metric interests the academic world mainly. Caveats are many; programs must meet special requirements to enable measurement.

As far as I know, only implements this metric.

This metric indicates whether each entry in a particular array has been referenced. This is useful for programs that are controlled by a finite state machine.

You can compare relative strengths when a stronger metric includes a weaker metric.

• includes since exercising every branch must lead to exercising every statement. This relationship only holds when control flows uninterrupted to the end of all basic blocks. For example a C/C++ function might never return to finish the calling basic block because it uses throw, abort, the exec family, exit, or longjmp.
• includes and (by definition).
• includes .
• includes and , as well as most other metrics.

Academia says the stronger metric subsumes the weaker metric.

Coverage metrics cannot be compared quantitatively.

Each project must choose a minimum percent coverage for release criteria based on available testing resources and the importance of preventing post-release failures. Clearly, safety-critical software should have a high goal. You might set a higher coverage goal for unit testing than for system testing since a failure in lower-level code may affect multiple high-level callers.

Using , , or you generally want to attain 80%-90% coverage or more before releasing. Some people feel that setting any goal less than 100% coverage does not assure quality. However, you expend a lot of effort attaining coverage approaching 100%. The same effort might find more bugs in a different testing activity, such as formal technical review. Avoid setting a goal lower than 80%.

Choosing good intermediate coverage goals can greatly increase testing productivity.

Your highest level of testing productivity occurs when you find the most failures with the least effort. Effort is measured by the time required to create test cases, add them to your test suite and run them. It follows that you should use a coverage analysis strategy that increases coverage as fast as possible. This gives you the greatest probability of finding failures sooner rather than later. Figure 1 illustrates the coverage rates for high and low productivity. Figure 2 shows the corresponding failure discovery rates.

One strategy that usually increases coverage quickly is to first attain some coverage throughout the entire test program before striving for high coverage in any particular area. By briefly visiting each of the test program features, you are likely to find obvious or gross failures early. For example, suppose your application prints several types of documents, and a bug exists which completely prevents printing one (and only one) of the document types. If you first try printing one document of each type, you probably find this bug sooner than if you thoroughly test each document type one at a time by printing many documents of that type before moving on to the next type. The idea is to first look for failures that are easily found by minimal testing.

The sequence of coverage goals listed below illustrates a possible implementation of this strategy.

1. Invoke at least one function in 90% of the source files (or classes).
2. Invoke 90% of the functions.
3. Attain 90% in each function.
4. Attain 100% .

Notice we do not require 100% coverage in any of the initial goals. This allows you to defer testing the most difficult areas. This is crucial to maintaining high testing productivity; achieve maximum results with minimum effort.

Avoid using a weaker metric for an intermediate goal combined with a stronger metric for your release goal. Effectively, this allows the weaknesses in the weaker metric to decide which test cases to defer. Instead, use the stronger metric for all goals and allow the difficulty of the individual test cases help you decide whether to defer them.

Coverage analysis is a structural testing technique that helps eliminate gaps in a test suite. It helps most in the absence of a detailed, up-to-date requirements specification. is the best general-purpose metric for C, C++, and Java. Setting an intermediate goal of 100% coverage (of any type) can impede testing productivity. Before releasing, strive for 80%-90% or more coverage of statements, branches, or conditions.

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