How COBOL Code Can Benefit From Machine Learning Insight

How COBOL Code Can Benefit From Machine Learning Insight

Did you realize that you, as a Software developer, spends about 75% of your time Research and query to understand the code, fix errors and make necessary changes? No, don’t write new code but engage in the cumbersome and time-consuming endeavors of debugging and optimizing.

With each change, applications become increasingly complex, which leads to an increase in the importance of software development productivity, which is attracting more attention from the professional community. Whether the leadership of a startup is concerned with the costs of a software development team and wants to boost efficiency to get more done with less, or a company’s engineering leader “shakes up” their teams to improve production, questions about productivity inevitably arise.

While some tools can help improve productivity by suggesting the code to write, even while the developer is writing the code, software developers still have to use their brains to add new features, fix bugs, implement changes to meet regulatory requirements, address security needs, and solve challenging problems. engineering. But what if there was a tool that did some of the hardest things for you?

Enters COBOL matea program that uses artificial intelligence to automate the identification of specific lines of code that require attention – no matter how tangled that code is throughout the application. (The program initially supports COBOL programs but will support other programs soon as well.)

What developers do without realizing it

To be effective in maintaining and supporting complex critical systems and applications, software developers go to great lengths to ensure that the software continues to operate. By acquiring industry and institutional know-how and visualizing code to understand the intent of previous developers, engineers inherently understand the risks associated with modifying code. The more developers understand how different pieces of code communicate, the better they can understand the entire code base, which makes them more efficient at changing it.

To develop this understanding and create those concepts around the code, one common approach is to just read the code. Developers will often find one thing they know the code is doing and trace that behavior back from the end, creating a “mental map” of connections between different parts of the code.

Another way to bridge the knowledge gap is to use code search tools and static and dynamic analysis tools. These tools will also eventually require developers to simulate what the code does so that they can visualize the capabilities. This means that they mentally connect the pieces together.

Ask any seasoned developer new to the code base about their experience, and they will undoubtedly tell you that the process of understanding what all of the code does is cognitively intensive and time-consuming (remember the eye-opening stats in this article’s opening paragraph?).

How Today’s Tools Help

From bug localization to program fixing to code analysis and synthesis, many modern software development tools like Sourcegraph, SonarQube, and DeepCode can analyze huge code bases, suggest where to look in the source code and even indicate “bad code” to tackle. Whether it’s illustrating code with images, enabling developers to search for specific APIs, or figuring out how a piece of code isn’t optimally written, tools like these can help developers identify patterns in code more quickly. Yes, these tools help but not in the most cognitively challenging part of maintaining apps.

Most of today’s tools are not yet able to identify specific lines of code that need to be changed, and figuring out that information is a difficult cognitive feat. Even worse, some tools can suggest incorrect changes to code or provide false positives that send developers down rabbit holes they may not realize they’ve been failing in for a long time. Thus, even when using the best software development tools, developers still have to rely on their awareness to get rid of seemingly disconnected facts and to safely compile relevant pieces of code to make changes.

The code base is somewhat similar to a novel

Imagine a mystery murder novel as the base of the code. In this scenario, when readers (developers) want to determine where the murder occurred (what behavior to change in the code), they have to look at more than just one page (throughout the code base). In such novels, there is often more than one plot that readers must follow. Whether the main character gets married or moves away, readers need to understand how the different elements on all the different pages work together to build a whodunit story. To find the location of the crime, they must also demystify those multiple stories, the murder weapon, possible suspects, the location of the murder and more.

Now imagine using a global software search tool to find out where the murder occurred in the novel. Such a tool might ask readers to look at certain pages, but then readers would have to go back and read all those pages to determine if the result is relevant to the story they are interested in in the novel. And then they still have to reassemble the story because it may not unfold in order or relate to previous passages.

But there is a better way! What if you could request a tool for a story you want and get it back? Enter fellow COBOL.

COBOL colleague: an artificially intelligent co-worker

Powered by Phase change programCOBOL Colleague uses a unique AI approach to automate developers’ thought processes. Using source code as the tool’s sole input, COBOL Colleague instantly empowers organizations with application expertise and intellectual control over their applications and systems – whether written in COBOL or (in the future) any other programming language.

The solution quickly improves developer efficiency by quickly moving developers to the exact code that needs to be changed and presenting that code in the order of execution, along with the data needed to replicate the behavior. Using cognitive automation to bridge the software knowledge gap at machine speed, COBOL Colleague reinterprets code into accurate, easy-to-understand concepts much faster than humans think, and then makes those concepts accessible to developers for their maintenance activities.

Benefit machine learning avatar In application code, a fellow COBOL “thinks” of the code the same way humans think – in terms of cause and effect. Basically, this code reflects as use cases, which is what developers ultimately strive to work with. By turning the code repository into a knowledge repository that developers can query, the unique AI enables software developers to interact with a collaborative agent to find the code they need instead of digging through millions of lines of code and bundling it together in their head.

Using a murder mystery analogy, the developer asks for the story from the book and gets the story they need.

With COBOL Colleague, developers like you can now isolate flaws, identify code to change, and mitigate risks involved in digital transformation and modernization. While Colleague isn’t yet advanced enough to rebuild or write code for you, the tool enables developers to focus on what brings their organizations the most value to code changes.

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