GitHub Copilot is presented as an AI pair programmer, that plugs into the user's editor. It then turns natural language prompts into code, offers multi-line function suggestions, speeds up test generation, filters out common vulnerable coding patterns, and blocks suggestions matching public code.
$10
per month
IBM watsonx Code Assistant Portfolio
Score 8.9 out of 10
N/A
IBM watsonx™ Code Assistant for Red Hat® Ansible® Lightspeed demystifies the process of Ansible Playbook creation through generative AI-powered content recommendations. Purpose-built to accelerate IT Automation, the product is designed to deliver automation content recommendations for an enhanced Ansible experience.
Copilit is fantastic at the following: 1. Solving simple, well-defined problems, such as implementing an algorithm, manipulating a data structure, or string manipulation and regex. 2. Implementing simple APIs that are mainly CRUD in nature, with moderate business logic inside them, which may involve some processing or passing the data through an algorithm. 3. Implementation of well-defined activities, such as implementing a connection to an Oracle DB using Hibernate or JDBC, or implementing boilerplate code for a backend service to listen to Kafka events. It is not that great when it comes to understanding and implementing code in a proprietary DSL. It struggles when implementing a major feature across a complex codebase. I believe developers should also adopt the trust-but-verify paradigm when expecting highly secure or regulated code from GitHub Copilot.
I would recommend for understanding your Mainframe components not for the GenAI piece involved from just my experience. The explanations were not up to the quality we wanted but its deterministic side provided a lot of value for different members of my team. The visuals would be great. I am not sure where it currently stands
It can automatically revamp specific parts of the COBOL code and very useful when we want to maintain the existing codebase but improve its structure. I can highlight a block of COBOL code and use Watsonx Assistant to suggest ways to simplify and optimize it.
Legacy codes, mostly written in COBOL, are cryptic and difficult to understand. Watsonx Assistant analyzes the code and provides insights into its functionalities and dependencies. A great help when working on older applications where understanding the codebase is crucial.
A step-by-step approach to modernize our applications slowly and steadily, so that we can control the process better. I don't have to change everything at once. Instead, I can focus on specific COBOL modules and automatically convert them to Java.
I feel that GitHub Copilot's overall usability is good due to its tight integration with Visual Studio and the workspace. However, developers expect greater ease of use, as there is a learning curve to realize productivity gains with the tool fully. I think there is room for improvement in GitHub Copilot's UI integration within Visual Studio.
It is useful that copilot integrates so well with vscode, which is a very common IDE. I used Tabnine for a little while but it was not that intuitive, and did not seem as helpful as GitHub copilot was. I have enjoyed GitHub copilot a lot, especially the ease of hitting the tab key and seeing quick progress in my tasks.
Security is very important in the mainframe world. At Watsonx, we work in the trusted Z environment, which has strong security rules, stricter than those of other cloud-based solutions. My domain is primarily mainframe modernization and Watsonx Code Assistant for Z is specifically used to understand and work with COBOL, the language used majorly in mainframe environments, not any general-purpose language that used in various platforms. It understands the nuances of COBOL and Assembler specific to the Z environment, something crucial for my work.
While manual review and adjustments are still needed, it's a 50-70% reduction in manual coding. Think about it - a project estimated to take a year is done in 4-6 months.
We've been able to introduce new features and improvements more quickly by updating our technology faster. One relevant example is we recently released an important update to our main product 45 days earlier than planned.
It has been a smart move and it's really paid off for our company. We've cut down a lot of time we used to spend doing things manually. We now spend our resources more wisely, work faster and finish projects sooner and as a result, we've reduced our development costs by 25%.
