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What is GitAuto?

GitAuto is a coding assistant powered by artificial intelligence that integrates effortlessly with GitHub (and optionally with Jira) to analyze backlog tickets or issues, inspect the structure and code of your repository, and independently generate and evaluate pull requests, typically completing these tasks in about three minutes per ticket. It excels at managing bug resolutions, fulfilling feature requests, and improving test coverage. Users can activate it via designated issue labels or selections on a dashboard, enabling it to generate code or unit tests, start a pull request, run GitHub Actions, and automatically fix failing tests until they pass. Supporting a diverse range of ten programming languages, including Python, Go, Rust, and Java, GitAuto offers free basic services while also providing paid plans for those needing a higher volume of pull requests and more enterprise-level features. With a commitment to a stringent zero data-retention policy, it processes your code through OpenAI without saving any of your data. Designed to accelerate delivery, GitAuto allows teams to tackle technical debt and backlogs without relying heavily on engineering resources, functioning as an AI backend engineer that drafts, tests, and refines code, ultimately leading to a remarkable boost in development productivity. This cutting-edge tool not only simplifies workflows but also enables teams to dedicate their efforts to more critical and strategic initiatives, fostering a more innovative environment.

What is Deequ?

Deequ is a groundbreaking library designed to enhance Apache Spark by enabling "unit tests for data," which helps evaluate the quality of large datasets. User feedback and contributions are highly encouraged as we strive to improve the library. The operation of Deequ requires Java 8, and it is crucial to recognize that version 2.x of Deequ is only compatible with Spark 3.1, creating a dependency between the two. Users of older Spark versions should opt for Deequ 1.x, which is available in the legacy-spark-3.0 branch. Moreover, we also provide legacy releases that support Apache Spark versions from 2.2.x to 3.0.x. The Spark versions 2.2.x and 2.3.x utilize Scala 2.11, while the 2.4.x, 3.0.x, and 3.1.x releases rely on Scala 2.12. Deequ's main objective is to conduct "unit-testing" on data to pinpoint possible issues at an early stage, thereby ensuring that mistakes are rectified before the data is utilized by consuming systems or machine learning algorithms. In the upcoming sections, we will illustrate a straightforward example that showcases the essential features of our library, emphasizing its user-friendly nature and its role in preserving data quality. This example will also reveal how Deequ can simplify the process of maintaining high standards in data management.

Media

Media

Integrations Supported

CSS
ChatGPT
Claude
Elixir
Gemini
GitBucket
GitLab
Go
HTML
Java
JavaScript
Jira
Julia
Next.js
OpenAI
OpenAI o4-mini
Python
Ruby
Rust
Tailwind CSS

Integrations Supported

CSS
ChatGPT
Claude
Elixir
Gemini
GitBucket
GitLab
Go
HTML
Java
JavaScript
Jira
Julia
Next.js
OpenAI
OpenAI o4-mini
Python
Ruby
Rust
Tailwind CSS

API Availability

Has API

API Availability

Has API

Pricing Information

$100 per month
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

GitAuto

Date Founded

2024

Company Location

United States

Company Website

gitauto.ai/

Company Facts

Organization Name

Deequ

Company Website

github.com/awslabs/deequ

Categories and Features

Categories and Features

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