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What is CodePeer?

CodePeer serves as a powerful static analysis toolkit specifically tailored for the Ada programming language, allowing developers to gain deep insights into their code while crafting more secure and resilient software applications. This advanced source code analysis tool excels at pinpointing potential logic and run-time errors, enabling the detection of bugs before the program runs, and functions as an automated peer reviewer that streamlines the error detection process throughout the entire development lifecycle. By employing CodePeer, developers are able to elevate code quality and facilitate comprehensive safety and security evaluations. This application operates independently on both Windows and Linux platforms, and it can be used in conjunction with any standard Ada compiler, or effortlessly integrated into the GNAT Pro development framework. Additionally, CodePeer effectively identifies a range of critical vulnerabilities found in the "Top 25 Most Dangerous Software Errors" cataloged in the Common Weakness Enumeration. It accommodates all Ada programming iterations, including versions 83, 95, 2005, and 2012. Noteworthy is CodePeer's recognition as a Verification Tool under the DO-178B and EN 50128 software standards, rendering it a trustworthy resource for developers committed to meeting stringent safety requirements. Moreover, the tool empowers users to proactively tackle potential issues, ultimately cultivating a more streamlined and confident approach to the development process. With its extensive capabilities, CodePeer stands out as an invaluable asset for any software development team focused on enhancing both quality and security.

What is Amazon CodeGuru?

Amazon CodeGuru is a cutting-edge tool designed for developers that employs machine learning to provide valuable recommendations for improving code quality while identifying the most expensive lines of code within an application. By integrating Amazon CodeGuru into your existing software development workflow, you can take advantage of automated code reviews that help highlight and enhance these costly segments of code, ultimately leading to lower overall expenses. In addition, Amazon CodeGuru Profiler aids developers by pinpointing the most resource-demanding lines of code, offering detailed visual representations and actionable advice for optimizing the code to save costs. Moreover, Amazon CodeGuru Reviewer uses machine learning techniques to uncover critical issues and subtle bugs during the application development process, thus improving the code's overall quality. This all-encompassing strategy not only simplifies the development process but also cultivates a more efficient and economically viable coding environment. By leveraging these tools, developers can significantly enhance both their productivity and the reliability of their applications.

Media

Media

Integrations Supported

Java
Python
Ada
C
C++
HTML
Jenkins
PikeOS
Seeker
Simulink
SonarQube Server

Integrations Supported

Java
Python
AWS App Mesh
AWS CodeCommit
AWS Fargate
AWS Lambda
Amazon EC2
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Bitbucket
GitHub

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

AdaCore

Date Founded

1994

Company Location

France

Company Website

www.adacore.com/codepeer

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/codeguru/

Categories and Features

Categories and Features

AI Code Review

Not specified

Application Performance Monitoring (APM)

Baseline Manager
Full Transaction Diagnostics
Performance Control
Resource Management
Root-Cause Diagnosis
Server Performance

Code Quality

Not specified

Machine Learning

Deep Learning
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Templates
Visualization

Static Code Analysis

Not specified

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