Find your most expensive lines of code with Amazon CodeGuru
Amazon CodeGuru is a developer tool that uses the power of machine learning to help you improve your code maintainability and find your most expensive lines of code. It performs automated code reviews and provides application performance recommendations.
Amazon CodeGuru Reviewer
Amazon CodeGuru Reviewer finds issues in your Java and Python code and recommends how to remediate them. For example, CodeGuru Reviewer detects deviation from best practices for using AWS APIs and SDKs, and also identifies concurrency issues, resource leaks, security vulnerabilities and incorrect input validation. To begin reviewing code, you can associate your existing code repositories on GitHub, GitHub Enterprise, Bitbucket or AWS CodeCommit with CodeGuru.
CodeGuru Reviewer identifies code maintainability issues and security risks (including top 10 OWASP categories) by automatically analyzing source code pull requests to find critical issues. It thenprovides intelligent recommendations for resolving code defects directly within the pull request. CodeGuru Reviewer identifies code maintainability issues in nine broad categories and equips your development team to maintain a high bar of coding standards in the software development process:
• AWS best practices: Correct use of AWS APIs (example polling, pagination)
• Java and Python Best Practices: Correct use of popular Java and Python language and library features
• Concurrency: Detects missing synchronization resulting in incorrect functionality or excessive synchronization leading to performance issues.
• Code maintainability: identifies code complexities or any characteristic in the source code that makes the code harder to maintain overtime
• Resource leaks: Correct handling of resources (example: releasing database connections)
• Sensitive information leaks: Leakage of Sensitive Information (example: logging AWS account credentials in plain text)
• Common coding best practices : checks for parameters and looks for lines of code that could create bugs (example: forgetting to check whether an object is null before setting it, reassigning a synchronized object, or forgetting to initialize a variable along an exception path).
• Code Cloning: Identifies duplicated code that could be consolidated for better code maintainability
• Input Validation: Checks for malformed or malicious data from untrusted sources
Codeguru Reviewer helps you improve code security and provides recommendations for best practices. It uses automated reasoning to analyze data flow from source to sink and across multiple functions to detect hard-to-find security vulnerabilities. The Security Detector supports Java, through Java 11 and identifies several categories of issues such as:
- AWS API Security Best Practices: you can check API security for AWS EC2 and KMS
- Java Crypto Library Best Practice: you can check if Javax.Crypto.Cipher is initialized and called correctly
- Python Crypto Library Best Practices: you can check that if correct versions of Python hashing and cryptography algorithms are used.
- Secure Web Applications: you can check web app related security issues, such as LDAP injections
- Sensitive Information Leak: you can check if there is any leakage of personal or sensitive information
- AWS Security Best Practices (such as AWS Crypto recommendations): you can check if your code meets AWS best practices
You can go to the CodeGuru console and trigger a security analysis on your entire repository or codebase by uploading you source and build artifacts.
You can view all code reviews in the “Code reviews” console page (Reviewer section). The page lists all code review information such as, the status of the code review, the repository, the number of recommendations, and more. You can click on a successfully completed code review to view recommendation details, search for recommendations and see the number of lines analyzed. You may also give feedback on CodeGuru recommendations by clicking on the thumbs up or thumbs down icon below the recommendation.
With CodeGuru, you can get ML-powered code review recommendations for all lines of code in the associated repositories (not just incremental changes through pull requests), under a specified code branch. You can run full repository scans to get code review recommendations during code migration, code due diligence and periodic code maintainability initiatives. In the CodeGuru console, you can navigate to the "Repository Analysis" tab in the "Code Reviews" page to trigger a new analysis on a full repository. Two full repository scans come included with the new repository size-based pricing model. For more details, visit the CodeGuru Pricing Page.
With pull request and full repository analysis available, onboarding onto CodeGuru Reviewer can help you 1) associate your repository, 2) initiate a full repository analysis, 3) continuously analyze pull requests for incremental code changes and 4) do a periodic re-scan of the full repository to ensure code maintainability.
CodeGuru Reviewer can be easily integrated into your CI/CD pipeline without you ever having leave your source code management or CI/CD tool. You can configure it to run and provide recommendations on a pull, push, or scheduled run of your pipeline. After you trigger a CodeGuru Reviewer scan via CI/CD, you can view your code quality and security recommendations within the CodeGuru Reviewer Console or within your CI/CD provider’s user interface. With CI/CD integration, you have peace of mind from being able to continuously monitor the quality and security of your code.
You can use CodeGuru Reviewer's GitHub Action located on the GitHub Marketplace to run security reviews and receive recommendations directly within the GitHub user interface. Once your onboarded, recommendations will show directly within the GitHub Security tab.
Recommendations are also provided within the pull request and within the AWS Console. If you click into a recommendation within GitHub, you get more in depth information on the finding, such as the the issue it creates within your application, the path to resolution, any CWEs (Common Weakness Enumerations) linked to it, and its severity.
Amazon CodeGuru Profiler
Amazon CodeGuru Profiler is always searching for application performance optimizations, identifying your most “expensive” lines of code and recommending ways to fix them to reduce CPU utilization, cut compute costs, and improve application performance. For example, CodeGuru Profiler can identify when your application is consuming excessive CPU capacity on a logging routine instead of executing on core business logic.
CodeGuru Profiler is designed to continuously run on production with minimal overhead which means you can leave it on all the time with minimal impact on application performance. It enables you to profile and troubleshoot your application using real customer traffic patterns and easily discover performance issues. With the profiler data and ML-powered recommendations, you can identify and fix performance issues for your applications in production. CodeGuru Profiler also provides a heap summary, so you can identify what objects are using up memory at any given time.
CodeGuru Profiler continuously analyzes application CPU utilization, heap usage, and latency characteristics to show you where you are spending the most cycles or time in your application. The CPU and latency analysis is presented in an interactive flame graph that helps you easily understand which code paths consume the most resources, verify that your application is performing as expected, and uncover areas that can be optimized further.
Flame graphs visualize the performance of your application by aggregating stack trace samples over a period of time to produce an accurate picture of the application's behavior during that time. You can use a flame graph to understand which paths consume the most resources, verify that your application is performing as expected, and uncover areas that can be optimized further. For example, method UploadGreyImage is spending $134,868 per year and this is consuming 10.22% wall clock time, so if you didn’t expect it to spend so much time you should investigate.
The heap usage analysis is presented on a heap summary visualization which shows you what objects are allocated on your heap – whether your own domain classes or those owned by libraries or the JDK.
Heap summary visualizes all the objects allocated on the heap for a given period of time along with their size, count and time series. For example you can see on the time series graph that at 4:20pm there are two objects that start significantly growing (java.util.LinkedHashMap$Entry and java.land.UUID) which indicates a potential memory leak. If this upward trend continues it could lead to an out of memory situation if left unchecked.
CodeGuru Profiler automatically identifies performance issues in your application and provides ML-powered recommendations on how to remediate them. These recommendations help you identify and optimize the most expensive or resource intensive methods within your code without requiring you to be a performance engineering expert. These optimizations help you reduce the cost of your infrastructure, reduce latency, and improve your overall end user experience.
When it sees opportunities to optimize your application performance, Amazon CodeGuru Profiler explains why it is recommending a change, what’s causing the issue, how to resolve it, and where in the code this issue is impacting your application. This recommendation shows you that this expensive line of code costs you $182.16K per year and has a 2.97% impact on your CPU utilization. If you follow the suggested resolution steps you will be able to save up to $182.16K.
Amazon CodeGuru Profiler continuously analyzes your application profiles in real-time and detects anomalies in the behavior of your application and its methods. Each anomaly is tracked in the Recommendation report of the CodeGuru Profiler console and you can see time series of how the method’s latency behaves over time with anomalies clearly highlighted. If configured, an Amazon SNS notification will also be sent when a new anomaly is detected.