How COBOL Code Can Benefit from Machine Learning Insight

Did you know that you as Software developers spend about 75% of their time Browse and query to understand code, troubleshoot, and make necessary changes? No, don’t write new code, just take care of the cumbersome, time-consuming efforts of debugging and improving.

With each change, the applications become more and more complex, increasing the importance of software development productivity, which is attracting more and more attention from the professional community. Whether the leadership of a startup is concerned about the cost of the software development team and wants to increase efficiency to do more with less, or a technical leader of a company is “shaking” their teams to improve performance, inevitably arise Questions about productivity.

While some tools can improve productivity by suggesting what code to write, even when the developer is writing code, software developers still need to use their brains to add new features, fix bugs, implement changes to meet regulatory requirements meet security requirements and find solutions to challenging technical problems. But what if there was a tool that did some of the hardest thinking for you?

Enter COBOL colleagueSoftware that uses artificial intelligence to automate the identification of specific lines of code that need attention—regardless of how entangled that code may be throughout the application. (The software initially supports COBOL programs, but will soon support others.)

What software developers do without realizing it

To be effective in maintaining and supporting complex critical systems and applications, software developers do tedious work to ensure the software stays functional. By acquiring specialized industry and institutional domain knowledge and conceptualizing the code to understand the intent of previous developers, engineers inherently understand the risks involved in changing the code. The more developers understand how different pieces of code connect together, the better they understand the overall code base and can modify it more effectively.

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

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Another method to fill the knowledge gap is to use code search tools and static and dynamic analysis tools. These tools also ultimately require developers to simulate what the code is doing so they can design the capabilities. That is, they mentally tie the pieces together.

Ask any experienced developer new to a codebase about their experiences, and they will no doubt tell you that the process of understanding what all the code does is cognitively intensive and very time-consuming (think of the revealing stats in this article). Introduction?).

How today’s tools help

From error location to program repair to code analysis and synthesis, many modern software development tools such as Sourcegraph, SonarQube, and DeepCode can analyze huge codebases, suggest where to look in the source code, and even signal potential “bad code” that needs to be addressed. Whether it’s visualizing code with images, allowing developers to find specific APIs, or identifying why a section of code isn’t written optimally, tools like these can help developers spot patterns in code faster. Yes, these tools help, but not with the most cognitively difficult part of maintaining applications.

Most of today’s tools are not yet able to identify the specific lines of code that need to be changed, and finding that information is hard cognitive work. Worse, some tools can even suggest false code changes or provide false positives, sending developers down rabbit holes they may not realize they’ve fallen down for a long time. As a result, even when using the best software development tools, developers must still rely on their cognition to sort through seemingly disjointed facts and piece together the relevant pieces of code to make changes safely.

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A codebase is a bit like a novel

Imagine that a detective story is the code base. In this scenario, if readers (developers) want to determine where the murder took place (what behavior needs to be changed in the code), they have to look at more than one page (the entire codebase). In such novels, too, there is often more than one plot for the reader to follow. Regardless of whether the main character is getting married or moving away, readers need to understand how the different elements on all the different pages work together to build the crime narrative. In order to find out where the murder took place, they must also make clear these multiple storylines, the murder weapon, potential suspects, the location of the murder and more.

Now imagine using a universal code search tool to find where in the novel the murder took place. Such a tool can direct readers to look at specific pages, but then readers have to go back and read all of those pages to determine if the hit is relevant to the plot they care about in the novel. And then they still have to reassemble the plot because it may not unfold in the right order or hark back to earlier passages.

But there is a better way! What if you could request a tool for the action you want and just get it back? Enter the COBOL colleague.

COBOL colleague: An artificially intelligent employee

Supported by phase change software, COBOL Colleague uses a unique artificial intelligence approach to automate thought processes for developers. With the source code as the tool’s only input, COBOL Colleague provides organizations with immediate application expertise and intellectual control over their applications and systems – whether written in COBOL or (in the future) another programming language.

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

use Symbolic machine learning When it comes to application code, the COBOL colleague “thinks” about the code in the same way that people do – in terms of cause and effect. Essentially, this reflects the code as use cases that developers ultimately want to work with. By transforming 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, rather than digging through millions of lines of code and piecing it together in their heads .

Using the crime analogy, the developer asks for the plot from the book and gets the plot needed.

With COBOL Colleague, developers like you can now isolate bugs, identify the code to change, and mitigate the risk associated with digital transformation and modernization. While Colleague isn’t mature enough to reverse engineer or write code for you, the tool empowers developers to focus on what brings the most value to their organizations—making code changes.

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