How AI Can Improve Software Development

Change is constant and inevitable. While some changes are welcome, others are just a necessary evil.

Constant calls for digitization combined with a pandemic-driven focus on transforming business operations are forcing organizations across industries to modernize their legacy systems. The emergence of cloud computing has reduced the on-premises IT footprints of enterprises. However, with 96 of the world’s 100 largest banks, 90% of the world’s largest insurance companies, 23 of the 25 largest retailers in the United States, and 71% of the Fortune 500 Still using mainframe systems, any modernization efforts – which increase the risk of downtime or defects – will often lead to disastrous consequences.

So, while making changes can be costly, uncertain and result in lost customers and/or embarrassment in the marketplace, modern business requires changing systems to continue to deliver value. However, the problem with modernizing the central computer is that today’s code searches, vectors and software analysis tools fall short when it comes to mitigating the risks associated with maintaining and improving systems.

Update: Cognitive problem

with 10,000 mainframes In active use globally, the average organization spends annually anywhere from 60% to 80% of IT budget Only on maintenance. But, as experienced and competent programmers retire or move forward, many companies are realizing that the knowledge of the niche area of ​​the industry and organization that these developers used to create and update these complex critical systems goes out the door with them.

Since the developers who originally wrote the code have long since advanced, and the documentation leaves much to be desired, new developers of a system are unable to gain comprehensive knowledge of the system’s functionality, at a detailed or fast enough level, to become adequately productive and efficient. This lack of speed comes with significant risk and cost to any organization.

Realizing the complete and correct intent of the function written in the code is not easy. Today’s developers spend Roughly 75% of their time Search through the source code to determine which code represents the function to change. To complicate matters further, understanding the code is not enough. To maintain and support any system effectively and efficiently, software developers must know precisely what the application is actually doing – and how changing code in one part of the system affects it as a whole. But since code that represents behavior that needs to change can be scattered throughout the system, developers might think they’re doing a minor tweak, when in fact they might break the whole system (and don’t even know). The risk of change leading to unintended consequences is real.

After all this, how can developers improve their efficiency and effectiveness?

Disadvantages of today’s tools

As code repositories grow unchecked over time to extraordinary sizes, those responsible for maintaining and maintaining system functionality say they have become Increasingly difficult to find errors in code without the help of the device. With debugging being a tedious and time-consuming endeavor, developers are increasingly turning to code finders, vectors, and static and dynamic analysis tools to analyze millions of lines of code, flag errors, and suggest solutions.

Whether the tool excels at bug localization, code visualization, or “bug” detection, many tools are completely inadequate when it comes to identifying specific lines of code that require attention. Program analysis tools only illustrate code in ways that developers have yet to interpret (perhaps incorrectly) and draw their own conclusions. While code search tools may speed up the rate at which developers can build a mental model of code, these tools are notorious for false positives, and developers still have to undergo the hard mental quest of tearing that mental model together to identify the code, and then make the changes safely. Even worse, code completion tools—the tools that less experienced developers rely on disproportionately—can actually suggest inaccurate changes (one notable Submit incorrect code 71% of the time), and this leads to risks.

The most important thing about this problem is that today’s tools do not have any way to effectively verify the scope and accuracy of the proposed change. The impact of a proposed change can be difficult to quantify, so a comprehensive validation test is often next to impossible.

Ideally, there should be a better way to mitigate risks while making changes to legacy systems. Enter artificial intelligence. By utilizing a unique approach to artificial intelligence, the code repository truly becomes a repository of knowledge, enabling many possibilities that have not been realized before.

Accessible AI-powered knowledge repository for developers

By taking advantage of AI to automate the identification of specific lines of code that require attention, developers can simply ask the AI-powered knowledge repository where behaviors come from — and quickly identify the code associated with that behavior. This puts the AI ​​squarely in the position of intelligence enhancement, which is key to taking advantage of its capabilities.

This new approach to artificial intelligence reinterprets what computation represents and turns it into concepts, thus “thinking” in code as humans do. The result is that software developers no longer have to discover the intent of previous developers encoded in the software to find potential errors. Better yet, developers can Overcoming the shortcomings of automated testing Using AI to verify that they haven’t broken the system before they compile or verify the code. The AI ​​will simulate the change and determine if it is isolated from the behavior under change. The result is that the limits of change are confined to the behavior under change so that unintended consequences do not arise.

Although AI is not advanced enough to safely make changes on its own, it can act as a repository of knowledge with the added ability to direct developers precisely where to make any needed changes. With AI, programmers – even on their first day on the job – can make any necessary repairs with confidence that they won’t break the entire system.

Unlike code search, code navigation, code completion, and software analysis tool, AI essentially enables developers to make any necessary changes safely, efficiently and effectively. With AI — powered by a knowledge repository that understands source code the same way a human does — developers can really do more with fewer resources.

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