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
One of the key advantages of static code analysis tools is their ability to identify security vulnerabilities in code. Software systems are prime targets for malicious attacks, and a single security vulnerability can have severe consequences for an organization. By using static code analysis tools to scan for vulnerabilities, developers can proactively address security issues before they can be exploited by attackers. This can ultimately save time, money, and reputation damage in the long run.
In addition to security vulnerabilities, static code analysis tools can also help developers improve the overall maintainability of their codebase. By providing insights into code complexity, duplication, and other code smells, these tools help developers write cleaner, more maintainable code. This can make it easier for developers to understand, modify, and extend existing code, leading to fewer bugs, quicker feature development, and overall higher code quality.
Static code analysis tools can also help enforce coding standards and best practices within development teams. By automatically flagging violations of coding guidelines, these tools can ensure that all developers are adhering to the same standards. This can lead to more consistent, readable, and reliable code that is easier for teams to collaborate on.
Furthermore, static code analysis tools can help improve the efficiency of code reviews. By automating the process of analyzing code for common issues, developers can focus their code review efforts on more complex problems that require human judgment. This can save time and ensure that code reviews are more effective in catching critical issues.
While static code analysis tools offer many benefits, it is important to note that they are not a replacement for human code review. These tools are meant to augment, not replace, the critical thinking and expertise of developers. Human judgment is still necessary to evaluate the context and impact of issues uncovered by static code analysis tools and to make decisions on how best to address them.
Overall, static code analysis tools are a valuable addition to any software development team’s toolbox. By helping developers identify security vulnerabilities, improve code quality, enforce coding standards, and enhance code reviews, these tools can ultimately save time, improve software reliability, and decrease technical debt. When used in conjunction with other best practices such as automated testing and continuous integration, static code analysis tools can contribute significantly to the success of software projects.
In Conclusion
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