Are you looking to add Static Code Analysis Tools to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Static Code Analysis Tools statistics of 2024.
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How much of an impact will Static Code Analysis Tools have on your day-to-day? or the day-to-day of your business? Should you invest in Static Code Analysis Tools? We will answer all your Static Code Analysis Tools related questions here.
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On this page, you’ll learn about the following:
Best Static Code Analysis Tools Statistics
☰ Use “CTRL+F” to quickly find statistics. There are total 27 Static Code Analysis Tools Statistics on this page 🙂Static Code Analysis Tools Software Statistics
- [ ] used PCA on a study that considered 38 software metrics for the open source projects Apache 1.3 and 2.0 to select a subset of nine principal components which explained 95% of the total data variance. [0]
Static Code Analysis Tools Latest Statistics
- This tool uses binary code/bytecode and hence ensures 100% test coverage. [1]
- According to McCabe, it possesses a cyclomatic complexity of 5, which is within boundaries. [2]
- Within the same time period in which 5,000 lines of Code were removed, 2.5% of Code duplications were added. [2]
- Errorfcalls and reports calls that contain a verb directive that is different than the new %w verb directive introduced in Go v1.13. [3]
- PHP Coding Standards Fixer— Fixes your code according to standards like PSR1, PSR 2, and the Symfony standard. [3]
- Most of the warnings coming from tools such as pylint, pep8 or pyflakes are likely to be a bit picky. [4]
- Let’s have a look at more featuresThe tool offers security feedback in real time and can cut mistakes made in new code by about 60 percent using an IDE scan. [5]
- The median scan time is just 90 seconds, and when combined with a low false positive rate of just 1.1 percent, it becomes easy to see why it is an efficient static code analysis tool. [5]
- The tool offers security feedback in real time and can cut mistakes made in new code by about 60 percent using an IDE scan. [5]
- A key strength of SAST tools is the ability to analyze 100% of the codebase. [6]
- I’m always recommending to follow this advice in 95% of your codebase. [7]
- flake8 bugbear finding likely bugs and design problems in your program. [7]
- The combination of code complexity metrics with static analysis fault density was used to predict the pre release fault density with an accuracy of 78.3%. [0]
- This combination was also used to separate high and low quality components with a classification accuracy of 79%. [0]
- [ ] used PCA to select a subset of five principal components out of 18 complexity metrics that account for 96% of the total variance in one of the studied commercial projects. [0]
- In this work we assume statistical significance at 99% confidence. [0]
- R2 R squared coefficient of determination, measures the variance in the predicted variable that is accounted by the regression built using the predictors. [0]
- Figure 1 shows that 4 principal components result in variance close to 98%. [0]
- We split our data into two parts 1) train data which accounts for 70% of the available data, and 2) test data representing the remaining 30%. [0]
- We first transform both train and test data to 4 components which explained 98% of the total data variance using PCA. [0]
- R2 measures the variance in the predicted variable that is accounted by the regression built using the predictors. [0]
- In order to address the fact that the above results are not by chance we repeated the data split (train 70% and test 30% of the data). [0]
- In order to compare the actual observed and predicted classes for each component, we categorized each predicted class into four individual categories as shown in Table 8. [0]
- High precision relates to a low false positive rate, meaning the probability to classify true fault prone components as non fault prone ones is low. [0]
- As shown in Figure 2 , the accuracy of the classification model lies at 79%. [0]
- You should get a 70–90% yield, with the review, spread over no more than 60–90 minutes. [8]
I know you want to use Static Code Analysis Tools, thus we made this list of best Static Code Analysis Tools. We also wrote about how to learn Static Code Analysis Tools and how to install Static Code Analysis Tools. Recently we wrote how to uninstall Static Code Analysis Tools for newbie users. Don’t forgot to check latest Static Code Analysis Toolsstatistics of 2024.
Reference
- scirp – https://www.scirp.org/journal/paperinformation.aspx?paperid=83690.
- softwaretestinghelp – https://www.softwaretestinghelp.com/tools/top-40-static-code-analysis-tools/.
- triology – https://www.triology.de/en/blog-entries/statistical-code-analysis-with-sonarqube.
- github – https://github.com/analysis-tools-dev/static-analysis.
- luminousmen – https://luminousmen.com/post/python-static-analysis-tools.
- comparitech – https://www.comparitech.com/net-admin/best-static-code-analysis-tools/.
- synopsys – https://www.synopsys.com/glossary/what-is-sast.html.
- towardsdatascience – https://towardsdatascience.com/static-code-analysis-for-python-bdce10b8d287.
- amoniac – https://amoniac.eu/blog/post/automatic-code-review-with-statistical-code-analysis.
How Useful is Static Code Analysis Tools
Static code analysis tools provide developers with automated analysis of their codebase without executing the code. These tools can detect a wide range of issues such as memory leaks, race conditions, bad coding practices, and potential vulnerabilities before the code is even run. This preemptive approach saves time and resources in the long run by catching errors early in the development process.
One of the key advantages of static code analysis tools is their ability to enforce coding standards consistently across a project. By setting rules and guidelines within these tools, developers can ensure that the codebase adheres to best practices and organizational standards. This results in cleaner, more maintainable code that is easier to understand and work with for both current and future developers.
These tools also help streamline the code review process by highlighting potential issues for code reviewers to focus on. This not only reduces the burden on developers to manually search for problems but also helps ensure that code reviews are more thorough and effective. By providing a comprehensive list of issues, static code analysis tools help prioritize and address critical areas that may otherwise be overlooked.
Furthermore, static code analysis tools can be integrated seamlessly into the development workflow, providing real-time feedback to developers as they write code. This immediate feedback loop allows developers to catch mistakes as they occur, encouraging better coding practices and preventing issues from proliferating throughout the codebase. Additionally, by identifying potential issues early on, these tools help prevent bugs from reaching production, ultimately enhancing the overall quality and reliability of the software.
However, it is important to note that while static code analysis tools offer numerous benefits, they are not a silver bullet for all code quality issues. These tools are limited in their ability to detect certain types of errors, such as logic errors or complex design flaws, which may require manual analysis or testing to uncover. Additionally, the effectiveness of these tools is highly dependent on the quality of the rules and configurations set by developers, as well as the tool’s ability to accurately interpret and analyze the code.
In conclusion, static code analysis tools are a valuable asset in the software development arsenal. They help maintain code quality, enforce coding standards, streamline code reviews, and prevent bugs from entering production. While not flawless, these tools play a crucial role in improving the overall reliability and maintainability of software projects. As technology continues to evolve, it is likely that static code analysis tools will only become more sophisticated and integral to the development process.
In Conclusion
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