Tag Archives: AWS Toolkit for JetBrains PyCharm

New Customization Capability in Amazon CodeWhisperer Generates Even Better Suggestions (Preview)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/new-customization-capability-in-amazon-codewhisperer-generates-even-better-suggestions-preview/

An AI coding companion, such as Amazon CodeWhisperer, aims to improve developers’ productivity by helping them write code quickly and securely. However, in particular cases, developers need to have code recommendations based on their internal libraries and APIs they extensively use every day.

As most of the existing AI coding companion tools are trained only on open-source codes, they lack the capability to customize the code recommendations using private code repositories. This limitation presents a variety of challenges for developers. Developers have a difficulty learning how to use internal libraries correctly and avoid security problems. For large codebases, it requires hours of reading documentation to understand what code needs to be written to complete the task.

Now in Preview —  Amazon CodeWhisperer Customization Capability
Today, I’m excited to announce Amazon CodeWhisperer customization capability (in preview) that enables organizations to customize CodeWhisperer to generate specific code recommendations from private code repositories. With this feature, developers who are part of Amazon CodeWhisperer Professional tier can now receive real-time code recommendations that include their internal libraries, APIs, packages, classes, and methods.

Let’s say that you’re a developer working for a hypothetical food delivery company called AnyCompany. You’re given a task to process a list of unassigned food deliveries around the driver’s current location. Previously, with CodeWhisperer, it would not know the correct internal APIs to process unassigned food deliveries or getting driver’s current location as this isn’t publicly available information. 

Now, with customization capability, you can ask CodeWhisperer to provide recommendations that include specific code related to the company’s internal services. The following screenshot shows how CodeWhisperer generates codes based on the internal codebase just by writing a set of comments.

With the customization capability of utilizing your internal codebase, CodeWhisperer now understands the intent, determines which internal and public APIs are best suited to the task, and generates code recommendations.

How It Works
The explanation above described how you can use CodeWhisperer customization capability as a developer. Now, let me share how it works and how you can get started. 

To create a customization, you need to complete the following steps as a CodeWhisperer administrator. 

  1. Administer your end users as CodeWhisperer administrator.
  2. Connect to existing repositories. You can connect one or more code repositories in your GitHub, GitLab, or BitBucket account using AWS CodeStar Connections or manually upload all of your codes into an Amazon Simple Storage Service (Amazon S3) bucket.
  3. Create a customization. CodeWhisperer will customize its model based on your codebase.
  4. Activate the customization for your team members. Once the customization is created, you can review and manually activate the customization to make it available automatically in your team members’ IDEs.

This capability provides two main advantages: providing real-time customized code recommendations that are specific to organizations and ensuring the protection of valuable intellectual property. Organizations can now promote the use of code that meets their quality and security standards based on their codes in existing repositories.

Furthermore, CodeWhisperer helps to ensure the security of your codes by providing the option to encrypt your customization data using customer managed keys in AWS Key Management Service (AWS KMS). This customization data will be deleted once the customization job finishes. 

Let’s Get Started
Let me show you how you can use the Amazon CodeWhisperer customization capability.

To get started, I need to create a customization. I need to have administrator access to navigate to the Create customization page on the Amazon CodeWhisperer dashboard.

On the Create customization page, I can connect the desired private code repositories I want CodeWhisperer to train. Currently, CodeWhisperer customization capability supports connection to GitHub, GitLab, and Bitbucket via AWS CodeStar Connections. If I have codes that are not in any code repositories, I can also manually upload my codes into an S3 bucket and define the Amazon S3 URI.

The following screenshot shows that I have existing connections with my code repositories using AWS CodeStar Connections. I can also create a new connection by selecting Create new connection.

Then, I can select Create Customization so CodeWhisperer can start training the model based on the codes available in the connection. The duration to complete this process depends on the size of the code repositories.

When the customization is ready, CodeWhisperer will not activate it automatically. This gives me the flexibility to activate the customizations just when I need them. But before I demonstrate that, I’d like to explain the evaluation score.

In short, the evaluation score helps me to measure the customization’s accuracy in predicting and providing code recommendations based on the codes in my code repositories. It provides a score in one of three categories: 1) Very Good, with a score ranging from 7–10; 2) Fair, with a score ranging from 4–7; and 3) Poor, with a score ranging from 0–4. It’s recommended to activate the customization if the evaluation score is 6 or higher. If the evaluation score is less than desired, I need to make sure that I’m providing enough codes for customization and provide a new code dataset that extensively contains references to internal APIs.

Here, I can see the Evaluation score for my customization is 8, and I’m happy with this result. Then, I can select Activate to start using this customization.

Once I have activated the customizations, I can define the access to selected customizations by selecting Add users. Now, I can give access to the customizations for selected team members who have been added as users for Amazon CodeWhisperer Professional tier. To do that, I can follow the guide from the Administering end users page. 

Then, once my team members sign in via AWS Toolkit in their IDEs, they will see the available customizations and can start using them. 

With Amazon CodeWhisperer, I can create multiple customizations by providing different code repositories. This feature is useful if I want to build customizations for code recommendations for certain teams. 

As administrator, I can also monitor the performance of each of the customizations by navigating to the CodeWhisperer dashboard page. This page summarizes useful data such as user activity, how many lines of code were suggested by CodeWhisperer and accepted by my team members, and how many security scans have successfully been run from IDEs. 

Amazon CodeWhisperer customization capability also follows the supported IDEs as part of AWS Toolkit by Amazon CodeWhisperer, such as Visual Studio Code, IntelliJ JetBrains, Visual Studio, and AWS Cloud9. This feature also provides support for most popular programming languages, including Python, Java, JavaScript, TypeScript, and C#.

Join the Public Preview
By securely leveraging customer’s internal codebase, Amazon CodeWhisperer unlocks the full potential of generative AI-powered coding that is customized to your unique requirements.

Join the public preview now and learn more on how to get started on the Amazon CodeWhisperer Customization page.

Happy coding!
— Donnie

Reimagine Software Development With CodeWhisperer as Your AI Coding Companion

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/reimagine-software-development-with-codewhisperer-as-your-ai-coding-companion/

In the few months since Amazon CodeWhisperer became generally available, many customers have used it to simplify and streamline the way they develop software. CodeWhisperer uses generative AI powered by a foundational model to understand the semantics and context of your code and provide relevant and useful suggestions. It can help build applications faster and more securely, and it can help at different levels, from small suggestions to writing full functions and unit tests that help decompose a complex problem into simpler tasks.

Imagine you want to improve your code test coverage or implement a fine-grained authorization model for your application. As you begin writing your code, CodeWhisperer is there, working alongside you. It understands your comments and existing code, providing real-time suggestions that can range from snippets to entire functions or classes. This immediate assistance adapts to your flow, reducing the need for context-switching to search for solutions or syntax tips. Using a code companion can enhance focus and productivity during the development process.

When you encounter an unfamiliar API, CodeWhisperer accelerates your work by offering relevant code suggestions. In addition, CodeWhisperer offers a comprehensive code scanning feature that can detect elusive vulnerabilities and provide suggestions to rectify them. This aligns with best practices such as those outlined by the Open Worldwide Application Security Project (OWASP). This makes coding not just more efficient, but also more secure and with an increased assurance in the quality of your work.

CodeWhisperer can also flag code suggestions that resemble open-source training data, and flag and remove problematic code that might be considered biased or unfair. It provides you with the associated open-source project’s repository URL and license, making it easier for you to review them and add attribution where necessary.

Here are a few examples of CodeWhisperer in action that span different areas of software development, from prototyping and onboarding to data analytics and permissions management.

CodeWhisperer Speeds Up Prototyping and Onboarding
One customer using CodeWhisperer in an interesting way is BUILDSTR, a consultancy that provides cloud engineering services focused on platform development and modernization. They use Node.js and Python in the backend and mainly React in the frontend.

I talked with Kyle Hines, co-founder of BUILDSTR, who said, “leveraging CodeWhisperer across different types of development projects for different customers, we’ve seen a huge impact in prototyping. For example, we are impressed by how quickly we are able to create templates for AWS Lambda functions interacting with other AWS services such as Amazon DynamoDB.” Kyle said their prototyping now takes 40% less time, and they noticed a reduction of more than 50% in the number of vulnerabilities present in customer environments.

Screenshot of a code editor using CodeWhisperer to generate the handler of an AWS Lambda function.

Kyle added, “Because hiring and developing new talent is a perpetual process for consultancies, we leveraged CodeWhisperer for onboarding new developers and it helps BUILDSTR Academy reduce the time and complexity for onboarding by more than 20%.”

CodeWhisperer for Exploratory Data Analysis
Wendy Wong is a business performance analyst building data pipelines at Service NSW and agile projects in AI. For her contributions to the community, she’s also an AWS Data Hero. She says Amazon CodeWhisperer has significantly accelerated her exploratory data analysis process, when she is analyzing a dataset to get a summary of its main characteristics using statistics and visualization tools.

She finds CodeWhisperer to be a swift, user-friendly, and dependable coding companion that accurately infers her intent with each line of code she crafts, and ultimately aids in the enhancement of her code quality through its best practice suggestions.

“Using CodeWhisperer, building code feels so much easier when I don’t have to remember every detail as it will accurately autocomplete my code and comments,” she shared. “Earlier, it would take me 15 minutes to set up data preparation pre-processing tasks, but now I’m ready to go in 5 minutes.”

Screenshot of exploratory data analysis using Amazon CodeWhisperer in a Jupyter notebook.

Wendy says she has gained efficiency by delegating these repetitive tasks to CodeWhisperer, and she wrote a series of articles to explain how to use it to simplify exploratory data analysis.

Another tool used to explore data sets is SQL. Wendy is looking into how CodeWhisperer can help data engineers who are not SQL experts. For instance, she noticed they can just ask to “write multiple joins” or “write a subquery” to quickly get the correct syntax to use.

Asking Amazon CodeWhisperer to generate SQL syntax and code.

CodeWhisperer Accelerates Testing and Other Daily Tasks
I had the opportunity to spend some time with software engineers in the AWS Developer Relations Platform team. That’s the team that, among other things, builds and operates the community.aws website.

Screenshot of the community.aws website, built and operated by the AWS Developer Relations Platform team with some help from Amazon CodeWhisperer.

Nikitha Tejpal’s work primarily revolves around TypeScript, and CodeWhisperer aids her coding process by offering effective autocomplete suggestions that come up as she types. She said she specifically likes the way CodeWhisperer helps with unit tests.

“I can now focus on writing the positive tests, and then use a comment to have CodeWhisperer suggest negative tests for the same code,” she says. “In this way, I can write unit tests in 40% less time.”

Her colleague, Carlos Aller Estévez, relies on CodeWhisperer’s autocomplete feature to provide him with suggestions for a line or two to supplement his existing code, which he accepts or ignores based on his own discretion. Other times, he proactively leverages the predictive abilities of CodeWhisperer to write code for him. “If I want explicitly to get CodeWhisperer to code for me, I write a method signature with a comment describing what I want, and I wait for the autocomplete,” he explained.

For instance, when Carlos’s objective was to check if a user had permissions on a given path or any of its parent paths, CodeWhisperer provided a neat solution for part of the problem based on Carlos’s method signature and comment. The generated code checks the parent directories of a given resource, then creates a list of all possible parent paths. Carlos then implemented a simple permission check over each path to complete the implementation.

“CodeWhisperer helps with algorithms and implementation details so that I have more time to think about the big picture, such as business requirements, and create better solutions,” he added.

Code generated by CodeWhisperer based on method signature and comment.

CodeWhisperer is a Multilingual Team Player
CodeWhisperer is polyglot, supporting code generation for 15 programming languages: Python, Java, JavaScript, TypeScript, C#, Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell scripting, SQL, and Scala.

CodeWhisperer is also a team player. In addition to Visual Studio (VS) Code and the JetBrains family of IDEs (including IntelliJ, PyCharm, GoLand, CLion, PhpStorm, RubyMine, Rider, WebStorm, and DataGrip), CodeWhisperer is also available for JupyterLab, in AWS Cloud9, in the AWS Lambda console, and in Amazon SageMaker Studio.

At AWS, we are committed to helping our customers transform responsible AI from theory into practice by investing to build new services to meet the needs of our customers and make it easier for them to identify and mitigate bias, improve explainability, and help keep data private and secure.

You can use Amazon CodeWhisperer for free in the Individual Tier. See CodeWhisperer pricing for more information. To get started, follow these steps.

— Danilo

Amazon CodeWhisperer, Free for Individual Use, is Now Generally Available

Post Syndicated from Steve Roberts original https://aws.amazon.com/blogs/aws/amazon-codewhisperer-free-for-individual-use-is-now-generally-available/

Today, Amazon CodeWhisperer, a real-time AI coding companion, is generally available and also includes a CodeWhisperer Individual tier that’s free to use for all developers. Originally launched in preview last year, CodeWhisperer keeps developers in the zone and productive, helping them write code quickly and securely and without needing to break their flow by leaving their IDE to research something. Faced with creating code for complex and ever-changing environments, developers can improve their productivity and simplify their work by making use of CodeWhisperer inside their favorite IDEs, including Visual Studio Code, IntelliJ IDEA, and others. CodeWhisperer helps with creating code for routine or time-consuming, undifferentiated tasks, working with unfamiliar APIs or SDKs, making correct and effective use of AWS APIs, and other common coding scenarios such as reading and writing files, image processing, writing unit tests, and lots more.

Using just an email account, you can sign up and, in just a few minutes, become more productive writing code—and you don’t even need to be an AWS customer. For business users, CodeWhisperer offers a Professional tier that adds administrative features, like SSO and IAM Identity Center integration, policy control for referenced code suggestions, and higher limits on security scanning. And in addition to generating code suggestions for Python, Java, JavaScript, TypeScript, and C#, the generally available release also now supports Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell scripting, SQL, and Scala. CodeWhisperer is available to developers working in Visual Studio Code, IntelliJ IDEA, CLion, GoLand, WebStorm, Rider, PhpStorm, PyCharm, RubyMine, and DataGrip IDEs (when the appropriate AWS extensions for those IDEs are installed), or natively in AWS Cloud9 or AWS Lambda console.

Helping to keep developers in their flow is increasingly important as, facing increasing time pressure to get their work done, developers are often forced to break that flow to turn to an internet search, sites such as StackOverflow, or their colleagues for help in completing tasks. While this can help them obtain the starter code they need, it’s disruptive as they’ve had to leave their IDE environment to search or ask questions in a forum or find and ask a colleague—further adding to the disruption. Instead, CodeWhisperer meets developers where they are most productive, providing recommendations in real time as they write code or comments in their IDE. During the preview we ran a productivity challenge, and participants who used CodeWhisperer were 27% more likely to complete tasks successfully and did so an average of 57% faster than those who didn’t use CodeWhisperer.

Code generation from a comment in CodeWhisperer
Code generation from a comment

The code developers eventually locate may, however, contain issues such as hidden security vulnerabilities, be biased or unfair, or fail to handle open source responsibly. These issues won’t improve the developer’s productivity when they later have to resolve them. CodeWhisperer is the best coding companion when it comes to coding securely and using AI responsibly. To help you code responsibly, CodeWhisperer filters out code suggestions that might be considered biased or unfair, and it’s the only coding companion that can filter or flag code suggestions that may resemble particular open-source training data. It provides additional data for suggestions—for example, the repository URL and license—when code similar to training data is generated, helping lower the risk of using the code and enabling developers to reuse it with confidence.

Reference tracking in CodeWhisperer
Open-source reference tracking

CodeWhisperer is also the only AI coding companion to have security scanning for finding and suggesting remediations for hard-to-detect vulnerabilities, scanning both generated and developer-written code looking for vulnerabilities such as those in the top ten listed in the Open Web Application Security Project (OWASP). If it finds a vulnerability, CodeWhisperer provides suggestions to help remediate the issue.

Scanning for vulnerabilities in CodeWhisperer
Scanning for vulnerabilities

Code suggestions provided by CodeWhisperer are not specific to working with AWS. However, CodeWhisperer is optimized for the most-used AWS APIs, for example AWS Lambda, or Amazon Simple Storage Service (Amazon S3), making it the best coding companion for those building applications on AWS. While CodeWhisperer provides suggestions for general-purpose use cases across a variety of languages, the tuning performed using additional data on AWS APIs means you can be confident it is the highest quality, most accurate code generation you can get for working with AWS.

Meet Your new AI Code Companion Today
Amazon CodeWhisperer is generally available today to all developers—not just those with an AWS account or working with AWS—writing code in Python, Java, JavaScript, TypeScript, C#, Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell scripting, SQL, and Scala. You can sign up with just an email address, and, as I mentioned at the top of this post, CodeWhisperer offers an Individual tier that’s freely available to all developers. More information on the Individual tier, and pricing for the Professional tier, can be found at https://aws.amazon.com/codewhisperer/pricing. 

Now in Preview – Amazon CodeWhisperer- ML-Powered Coding Companion

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-in-preview-amazon-codewhisperer-ml-powered-coding-companion/

As I was getting ready to write this post I spent some time thinking about some of the coding tools that I have used over the course of my career. This includes the line-oriented editor that was an intrinsic part of the BASIC interpreter that I used in junior high school, the IBM keypunch that I used when I started college, various flavors of Emacs, and Visual Studio. The earliest editors were quite utilitarian, and grew in sophistication as CPU power become more plentiful. At first this increasing sophistication took the form of lexical assistance, such as dynamic completion of partially-entered variable and function names. Later editors were able to parse source code, and to offer assistance based on syntax and data types — Visual Studio‘s IntelliSense, for example. Each of these features broke new ground at the time, and each one had the same basic goal: to help developers to write better code while reducing routine and repetitive work.

Announcing CodeWhisperer
Today I would like to tell you about Amazon CodeWhisperer. Trained on billions of lines of code and powered by machine learning, CodeWhisperer has the same goal. Whether you are a student, a new developer, or an experienced professional, CodeWhisperer will help you to be more productive.

We are launching in preview form with support for multiple IDEs and languages. To get started, you simply install the proper AWS IDE Toolkit, enable the CodeWhisperer feature, enter your preview access code, and start typing:

CodeWhisperer will continually examine your code and your comments, and present you with syntactically correct recommendations. The recommendations are synthesized based on your coding style and variable names, and are not simply snippets.

CodeWhisperer uses multiple contextual clues to drive recommendations including the cursor location in the source code, code that precedes the cursor, comments, and code in other files in the same projects. You can use the recommendations as-is, or you can enhance and customize them as needed. As I mentioned earlier, we trained (and continue to train) CodeWhisperer on billions of lines of code drawn from open source repositories, internal Amazon repositories, API documentation, and forums.

CodeWhisperer in Action
I installed the CodeWhisperer preview in PyCharm and put it through its paces. Here are a few examples to show you what it can do. I want to build a list of prime numbers. I type # See if a number is pr. CodeWhisperer offers to complete this, and I press TAB (the actual key is specific to each IDE) to accept the recommendation:

On the next line, I press Alt-C (again, IDE-specific), and I can choose between a pair of function definitions. I accept the first one, and CodeWhisperer recommends the function body, and here’s what I have:

I write a for statement, and CodeWhisperer recommends the entire body of the loop:

CodeWhisperer can also help me to write code that accesses various AWS services. I start with # create S3 bucket and TAB-complete the rest:

I could show you many more cool examples, but you will learn more by simply joining the preview and taking CodeWhisperer for a spin.

Join the Preview
The preview supports code written in Python, Java, and JavaScript, using VS Code, IntelliJ IDEA, PyCharm, WebStorm, and AWS Cloud9. Support for the AWS Lambda Console is in the works and should be ready very soon.

Join the CodeWhisperer preview and let me know what you think!

— Jeff;