IBM Bob is an AI-assisted development tool designed to support developers across the software development lifecycle. It helps interpret developer intent and provides assistance when working with real-world codebases by using contextual awareness of the surrounding project.
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IBM watsonx Code Assistant Portfolio
Score 8.8 out of 10
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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.
The first time using it feels like magic. Its ability to analyze an existing codebase and adapt accordingly to questions, feature requests, issues, etc. is remarkable. However, it is not limited to an existing codebase - I've had it create new applications based on styling from another application from scratch. It has quickly gone from an amazing novelty to something that is relied upon daily.
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.
It works well, for the most part, but asking for authorization for multiple steps or tool installation and use sometimes becomes bothersome. You can grant blanket authorizations but I do prefer to have a tradeoff there so not all is bad, but sometimes I get the impression that we get asked the same question several times during the same workflow.
ChatGPT just did not have the breadth and depth of insight for my coding needs. To get an approximation of what I wanted I had to restate my prompt multiple times, and it looked almost like it did not learn from one time to the other.
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.
We've been able to grow our own development team from within as a result of using this technology. Team members who may not have been confident enough to meaningfully contribute to the existing codebase have now become high-output developers.
We've been able to grow an automated test suite to more thoroughly test our mobile application. What once took two weeks of manual regression testing can now be done in an afternoon.
Our documentation has improved considerably, giving the product a more polished feel. Product documentation had previously consistently lagged behind new feature development.
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%.
