Code Checker

A code checker is an automated software that statically analyzes source code and detects potential issues. Most code checkers provide in-depth insights into why a particular line of code was flagged to help software teams implement coding best practices. These code-level checks often measure the syntax, style, and documentation completeness of source code.

An AI-powered code checker allows organizations to detect and remediate more complex code issues earlier in the secure software development lifecycle (SSDLC). AI algorithms that have been trained by hundreds of thousands of open source projects to capture symbolic AI rules about possible issues and remediation. By leveraging this learned knowledge from the global open source development community, an AI engine can often detect quality and security issues that may not be caught during peer code reviews or pair programming. That means the efficiency of an AI-powered code checker enables developers to fix issues very early — before they reach production and potentially impact end-users.

A key part of DevSecOps is shifting left — or detecting and remediating vulnerabilities earlier in the development process. Implementing a code checker into your existing continuous integration and continuous delivery (CI/CD) pipeline is one of the most widely accepted best practices. Embedding static analysis into the IDE informs developers of vulnerabilities at the earliest possible moment — eliminating code security risks at the source.

Integrating code checkers into existing developer workflows is a great way to fix code issues earlier, while also helping developers learn about best practices. This can make a significant impact on the quality and security of code that developers write going forward. More maintainable code can also improve the customer experience because there are fewer bugs and technical debt to deal with in the future.

When it comes to static application security testing (SAST) with a code checker, it’s important to choose a developer-first tool that integrates into developer workflows and produces minimal false positives in scan results. A SAST tool also needs to take a comprehensive approach for scanning source code, and be able to combine with linters to check code syntax and style.

CONFIGURATION:
Ensures that application configuration files are following security best practices and policies.

SEMANTIC:
Examines code contextually to estimate what the developer intended, and check whether the code syntax differs.

DATA FLOW:
Tracks the flow of data from insecure sources to ensure it’s cleansed before consumption by the application.

STRUCTURAL ANALYSIS:
Determines whether there are inconsistencies with implementing language-specific best practices and cryptographic techniques.

The code checker you use should leverage a comprehensive vulnerability database to identify security issues at the code level, as well as known vulnerabilities introduced via open source dependencies. Vulnerability databases help developers stay on top of the latest security exploits as they’re discovered, without spending endless hours researching the current cyber threat landscape. This type of data-driven security works in tandem with threat intelligence to improve the overall security posture of your organization.

Finally, detecting code security issues is only half the battle. An effective code checker solution will identify flaws, while also giving developers the insights they need to remediate them. This should include the precise source of the issue, and any known publicly available fixes for both security flaws and code anti-patterns.

Whether you’re at the earliest stages of your code security journey or a seasoned pro, there are a few best practices that are always good to implement:

• Start scanning early in the SDLC to establish a baseline.

• Scan your code often throughout the various stages of the SDLC to catch any new or developing security issues.

• Be sure to scan your third-party dependencies, containers, and IaC configurations in addition to first-party code.

Code security can be described using the CIA triad — confidentiality, integrity, and availability. The CIA triad is often used as a model for secure systems, and to identify possible vulnerabilities and fixes. Today, applications consist of 80 to 90% open source dependencies. But the remaining 10 to 20% is critical: this code reflects your personal IP, and there is no open source community helping you keep it secure. The best practice is to accept the work of the open source community by scanning and updating software dependencies in your project using scanners like Snyk Open Source — while doing your part by scanning and fixing your code using Snyk Code.

• Confidentiality
  Secure software systems do not disclose information to parties that are not allowed to receive it. That includes malicious external actors as well as unauthorized internal stakeholders.

• Integrity
  Secure software systems make sure that data and processes are not tempered with, destroyed, or altered. Transactions succeed when all sub-transactions succeed, and the stored data does not contradict each other.

• Availability
  A secure system also needs to be able to be used in due time. Blocking a system by overloading parts of it renders the system useless and insecure.

Code quality is a subjective term, and means something different to every development team. In general, however, the quality of code relates to how closely it follows commonly accepted coding standards and best practices.

Here are five frequently used measures of code quality to consider when developers ask, how do I check my code?

1. Reusability
   It’s best to write code that’s highly reusable. For example, in object-oriented programming, it’s important to make classes and methods clean and modular, so that code is easier to debug and scale across projects. Restricting access to certain reusable blocks of code through encapsulation can also improve security.

2. Maintainability
   Along with being reusable, it’s important that source code is maintainable. As a codebase grows, complexity and technical debt often increase, leading to bugs that are difficult to pinpoint and slow development in the long run. Automated code analysis and peer reviews can ensure that developers are only pushing highly maintainable code into production.

3. Testability
   High-quality code should support testing efforts. Along with writing modular code that makes automated testing easier, developers need to prioritize clear and up-to-date documentation. This allows test engineers to more easily understand the purpose of a particular code snippet.

4. Consistency
   Code should be portable enough that it can run on any development, staging, or production environment without compatibility issues. Docker and other containerization platforms can help ensure code and dependencies are consistent across different deployment environments.

5. Reliability
   Software should be designed for reliability from the start. Meaning developers need to proactively prevent technical debt from accruing when they push code. Otherwise, software can become less reliable over time and have a decrease in availability, fault tolerance, data integrity, and ability to recover from outages. These lack of reliability can also have a negative impact on the security posture of an application.

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