How to use the command 'gcov' (with examples)

Gcov is a powerful code coverage analysis and profiling tool that is part of the GNU Compiler Collection (GCC). It helps developers discover untested parts of their program, enhancing the overall software testing process. When using gcov, developers can obtain a detailed report that maps code coverage, highlighting execution frequencies of various code segments. It facilitates the optimization of test suites by providing insights into which parts of the codebase have been exercised adequately by tests and which have not.

Generate a coverage report named file.cpp.gcov

Code:

gcov path/to/file.cpp

Motivation: Using gcov to generate a coverage report is a fundamental task in ensuring your code is well-tested. It provides a file annotated with the frequency of execution for each line, making it easy to identify which parts of the code need more rigorous testing. This is particularly useful in large projects where manually tracking untested code can be cumbersome.

Explanation:

• gcov: This is the command used to invoke the gcov tool.
• path/to/file.cpp: This specifies the path to the source file for which you want to generate the coverage report. Gcov processes the compiled code (with profiling enabled) to produce this report.

Example output: Upon running the command, a file named file.cpp.gcov will be generated in the same directory, containing annotated source code. The lines will be prefixed with execution counts, making it easy to identify how many times a particular line was executed during the test runs.

Write individual execution counts for every basic block

Code:

gcov --all-blocks path/to/file.cpp

Motivation: Understanding execution counts for every basic block within your code can be crucial for optimizing performance and test coverage. A basic block is a sequence of consecutive statements in which control enters at the beginning and leaves at the end without halt or possibility of branching except at the end. This insight helps in fine-tuning programs by revealing less frequently executed blocks which may need further inspection.

Explanation:

• --all-blocks: This argument tells gcov to provide execution counts for each basic block, rather than just the line-by-line coverage.
• path/to/file.cpp: This is the path to the source file being analyzed.

Example output: The output will include additional information in the coverage report, showing execution frequencies for each block. This additional level of detail allows developers to understand more complex execution paths within their code.

Write branch frequencies to the output file and print summary information to stdout as a percentage

Code:

gcov --branch-probabilities path/to/file.cpp

Motivation: Branches in code represent decision points, such as if/else and switch statements, which can be crucial in determining the logic paths taken by a program. By writing branch frequencies and printing a summary in percentages, developers can quickly assess the likelihood of each branch’s execution. This can help focus testing efforts on less likely branches that may otherwise go unchecked.

Explanation:

• --branch-probabilities: This flag instructs gcov to record the probabilities of branches being executed, in the form of percentages.
• path/to/file.cpp: Specifies the source file to analyze.

Example output: In addition to the usual line-by-line execution data, the output will include a summary identifying which branches are more or less frequently executed, presented in percentage terms. This provides a high-level view of branch execution probabilities.

Write branch frequencies as the number of branches taken, rather than the percentage

Code:

gcov --branch-counts path/to/file.cpp

Motivation: While percentages provide a relative understanding of branch execution, absolute counts can offer a clearer picture of how often specific branches are taken. This can be advantageous when examining functions with numerous branches or loops, ensuring that even infrequent paths are properly tested and validated.

Explanation:

• --branch-counts: This option changes the branch frequency data from percentages to absolute counts, offering raw execution numbers rather than probabilities.
• path/to/file.cpp: The path to the source file for which branch counts should be analyzed.

Example output: The resulting report will display the number of times each branch has been taken, granting precise insight into the execution of branching logic within the code.

Do not create a gcov output file

Code:

gcov --no-output path/to/file.cpp

Motivation: There are scenarios where developers may not need or want an output file but do wish to analyze execution data directly via standard output (stdout). This command is beneficial in situations where temporary analysis or integration with other tools is needed without generating persistent files.

Explanation:

• --no-output: This prevents gcov from creating any output annotation files.
• path/to/file.cpp: Points to the source file for coverage analysis.

Example output: Since no files are created, the analysis data will be printed directly to stdout, allowing developers to pipe it into other applications or scripts for further processing or real-time analysis.

Write file level as well as function level summaries

Code:

gcov --function-summaries path/to/file.cpp

Motivation: Detailed summaries that include both file and function-level coverage breakdowns can significantly aid in understanding the effectiveness of testing at a more granular level. Such summaries help identify which functions are well-tested within a file and which require more rigorous evaluation, allowing for targeted improvements in testing.

Explanation:

• --function-summaries: This argument enables gcov to include summaries that capture both file-wide and per-function coverage statistics.
• path/to/file.cpp: Indicates the path to the source file undergoing coverage analysis.

Example output: The generated output will contain comprehensive summaries detailing coverage metrics at both file and function levels. These summaries assist in a layered analysis of how well each function within the file is covered during testing.

Conclusion:

By leveraging the various functionalities of gcov, developers can gain deep insights into their code’s execution patterns. These insights assist in robustly enhancing test coverage, improving code reliability and performance, and ultimately contributing to higher quality software development practices.