Static Code Analysis
Introduction
Static analysis helps development teams that are under pressure. Quality releases needed to be delivered on time. Coding and compliance standards need to be met. And mistakes are not an option. That’s why development teams are using static analysis tools. Here, we discuss static analysis and the benefits of using a static code analysis tool. If you’re looking for guidance on integrating static code analyzers into your workflows, please refer Cloud Direct Connect.
What Is Static Code Analysis?
Static code analysis refers to a specific type of computer program debugging that can test the code without the need to execute the program. This enables a thorough comprehension of the code structure and can even help testers ensure that the code remains compliant with various industry standards. Static code analysis is a popular tool with development and quality assurance teams building new software. This kind of analysis can validate the code while hunting for vulnerabilities. It can help programmers pinpoint a variety of coding issues such as errors in program code, coding standard violations, undefined values, violations of syntax and most importantly, security vulnerabilities. For even better efficiency, developers can also take the help of automated tools in deploying static analysis. This can also help programmers to address the all-too-common problem of buffer overflows that generally are a result of weaknesses in source code.
Types of Static Code Analysis
SAST or static application security testing is a popular form of static code analysis that is especially relevant for application security testing. Programmers may need to use multiple coding standards, such as, MISRA, AUTOSAR, CERT, CWE, etc. in order to identify all error classes. There are many types of static code analysis that can be employed to detect a variety of errors. The complete list can be found at Cloud migration solution. Some of these include:
PERFORMANCE
Performance tests are conducted to detect errors that result in deterioration of overall performance. They can also be used to ensure that developers stay up to date with current best practices.
SECURITY
As the name suggests, security tests conducted to find weaknesses in the source code related to security. These could include typical security risks such as weak cryptography, configuration problems, and command injection errors specific to the framework
RELIABILITY
Reliability tests ensure that there are no problems with the functionality of the code. This makes sure that your software does not go unresponsive out of the blue during usage. Reliability tests are also very helpful in detecting memory leaks or threading problems.
STYLE
Programmers use stylistic static analysis to check for and ensure uniform coding styles. This confirms the ease of use and comprehensibility of the code as well as takes care of bug fixes. Programmers no longer have to look for individual instances of style violations that eat up a lot of time. This test automatically finds all relevant instances.
Benefits of Static Analysis Tools:
Depth of analysis
Testing is limited in its ability to check all potential code execution paths. A static code analyzer, on the other hand, can continually keep checking the code during builds. This results in a thorough and ongoing in-depth analysis of the code that can throw up all potentially problematic areas of the code or deviations from applied rules.
Speed of analysis
Manual code reviews always take a lot of time for developers to conduct. Automated tools take away most of this burden from their shoulders by making the whole process a lot faster. Static code checking also enables developers to spot and fix problems much earlier. Moreover, it empowers developers to know the exact location of the error so they don’t have to waste any time finding it. This results in a much faster error fixing and resolution process. Most importantly, the earlier you catch coding errors, the less you have to spend in remedying them.
Accuracy
As is universally acknowledged, manual code reviews often result in an expanded scope for errors. Automated tools reduce this scope by a significant margin as they scan through all lines of code in the software to detect problems. This helps programmers to maintain a high level of quality checking for all the code they develop before testing begins. Adhering to this level of code quality checking, also helps in maintaining code compliance with industry standards.
Cost-effective
Static code analysis can prove to deliver improved coverage, quicker results, and a lot lower number of false positives. All of these effectively contribute to static code analysis being much more cost effective for code development. This is especially true of cloud-based static code analysis tools that empower programmers with more improved in-context guidance about security flaws. This can also ensure that the assessments remain current with an evolving list of threats. The cloud-based static code analysis tool also removes the need for companies to invest in additional software, hardware and technical staff. All developers need to do is to simply upload the code and the analyzer takes care of quick analysis of errors along with available guidance on how to fix them. These kinds of platforms are also often integrated into IDEs and other development tools that allow for seamless integration of code security into existing workflows.
Static Code Analysis Best Practices
There are some best practices programmers can adopt while deploying static analyzers to ensure the highest standard of code quality. These include:
- Correctly define the scope of the problem
- Render the code usable and readable
- Improve on the scope of reusing the code
- The scope of adding new features remains available with extensibility should the application need them
- Improve on minimal resource utilization while developing code while still retaining high speed and quality of execution
- Make use of both dynamic and static analysis
- Employing static code analysis with all of the above best practices can result in a simplified approach towards identifying bugs and a better QA/QC process overall.
