How COBOL Code Can Benefit from Machine Learning Insight

Did you realize that you, as software developer, spends about 75% of his time research and query to understand code, fix bugs, and make necessary changes? No, not writing new code, but engaging in the tedious and time-consuming debugging and improvement efforts.

With each change, 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 management of a startup is worried about the cost of the software development team and wants to promote efficiency to do more with less, or whether a corporate engineering manager is “shaking up” their teams to improve performance, questions about productivity inevitably arise.

While some tools can help improve productivity by suggesting 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 safety needs and solve complex engineering problems. But what if there was a tool that could do the thinking for you?

Walk in COBOL colleaguesoftware that uses artificial intelligence to automate the identification of specific lines of code that need attention, no matter how entangled that code is in the application. (The software initially supports COBOL programs, but will soon support others as well.)

What Software Developers Do Without Realizing It

To be effective in maintaining and supporting complex critical systems and applications, software developers work hard to ensure that software remains functional. By gaining specialized industry and institutional domain knowledge and conceptualizing code to understand the intent of previous developers, engineers inherently understand the risks associated with changing code. The more developers understand how different pieces of code are connected, the better they will understand the whole codebase, which will make them more efficient at modifying it.

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

Another method to fill the knowledge gap is to use code search tools and static and dynamic analysis tools. These tools will also require developers to simulate what the code does so that they can conceptualize functionality. This means that they mentally attach the pieces together.

Ask any seasoned developer new to a codebase about their experience, and they’ll no doubt tell you that the process of understanding what all the code does is cognitively intensive and time-consuming (remember the statistic revealing in this article first paragraph?).

How Today’s Tools Help

From locating bugs and fixing programs to analyzing and synthesizing code, many modern software development tools such as Sourcegraph, SonarQube and DeepCode can analyze massive code bases, suggest where to look in the source code and even point out potential “bad code” that needs to be addressed. . Whether it’s illustrating code with images, allowing developers to find particular APIs, or acknowledging that a piece of code isn’t written optimally, tools like those -these can help developers identify patterns in code more quickly. Yes, these tools help, but not with the most cognitively difficult part of application maintenance.

Most current tools are not yet able to identify the specific lines of code that need to be changed, and unearthing this information is hard cognitive work. Worse, some tools may even suggest incorrect code changes or provide false positives that send developers down rabbit holes they might not realize they’ve fallen into for a long time. Therefore, even when using the best software development tools, developers still have to rely on their cognition to weed out seemingly disconnected facts and piece together the relevant pieces of code to make changes safely.

A codebase is like a novel

Imagine that a detective story is the basis of the code. In this scenario, when readers (developers) want to determine where the murder took place (what behavior in the code needs to change), they have to look on more than one page (in the whole code base). In such novels there is also often more than one plot line for readers to 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 the whodunit narrative. To find where the murder took place, they must also disambiguate these multiple scenarios, the murder weapon, potential suspects, the location of the murder and more.

Now imagine using a universal code finder to find out where the murder took place in the novel. Such a tool might tell readers to look on specific pages, but then readers would have to go back and read all of those pages to determine if the hit is relevant to the storyline they’re interested in in the novel. And then they still have to reassemble the script because it may not happen in order or relate to earlier passages.

But there is better! What if you could request a tool for the scenario you want and just pick it up? Enter COBOL Colleague.

COBOL colleague: an artificially intelligent colleague

Powered by Phase change software, COBOL Colleague uses a unique artificial intelligence approach to automate developer thinking processes. With source code as the only input to the tool, COBOL Colleague immediately gives organizations application expertise and intellectual control over their applications and systems, whether written in COBOL or (in the future) in any other programming language.

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

Using symbolic machine learning on application code, COBOL Colleague “thinks” about code the same way humans do – in terms of cause and effect. Essentially, it mirrors the code as a use case, which is what developers ultimately seek to work with. By transforming the code repository into a knowledge repository that developers can query, the unique AI allows software developers to interact with a collaborative agent to find the code they need instead of searching through millions of lines of code and piece it together in their heads.

Using the murder mystery analogy, the developer asks for the script of the book and gets the script it needs.

With COBOL Colleague, developers like you can now isolate defects, identify code that needs to be changed, and mitigate the risks associated with digital transformation and modernization. While Colleague isn’t yet sophisticated enough to rebuild or write code for you, the tool allows developers to focus on what brings the most value to their organization: making code changes.