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Author: Steve Brothers

  • How AI Can Improve Software Development

    How AI Can Improve Software Development

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

    Continuous calls for digitalization combined with a pandemic-prompted emphasis on transforming business operations, force organizations across industries to modernize their legacy systems. The emergence of cloud computing has shrunk organizations’ on-premise IT footprints. However, with 96 of the world’s largest 100 banks, 90% of the world’s largest insurance companies, 23 of the 25 largest retailers in the U.S. and 71% of the Fortune 500 still using mainframe systems, any modernization efforts—resulting in increased risk of downtime or defects—will frequently lead to disastrous consequences. 

    So, while making changes can be expensive, uncertain, and lead to customer loss and/or embarrassment in the marketplace, modern business demands that systems change to continue to deliver value. The problem with mainframe modernization, however, is that today’s code search tools, linters and program analysis tools are deficient when it comes to mitigating the risks associated with maintaining and improving systems. 

    Modernization: A Knowledge Problem

    With 10,000 mainframes in active use globally, the average organization annually spends anywhere from 60% to 80% of its IT budget just on maintenance. But, as experienced and proficient programmers retire or move on, many companies are realizing that the specialized domain knowledge of the industry and institution that those developers used to create and update these complex critical systems is walking out the door with them. 

    As the developers who originally wrote the code have long since moved on, and documentation leaves a lot to be desired, developers new to a system are unable to acquire comprehensive knowledge of the system’s functionality, at a detailed or fast enough level, to become adequately productive and proficient. That lack of speed comes with significant risk and cost to any organization.

    Recognizing the full and true intent of the functionality written in code is not simple. Developers today spend roughly 75% of their time searching through source code, to identify the code representing the functionality that needs to be changed. Complicating matters further, understanding the code is not enough. To effectively and efficiently maintain and support any system, software developers must know precisely what an application actually does—and how altering code in one part of the system impacts it as a whole. But since the code representing a behavior that needs to be changed can be littered throughout the system, developers might think they’re making a simple tweak, when really they may be breaking the entire system (and not even knowing it). The risk of change resulting in unintended consequences is real.

    All of that being said, how can developers improve their efficiency and effectiveness?

    The Shortcomings of Today’s Tools

    With code repositories growing unchecked over time to extraordinary sizes, those responsible for maintaining and preserving a system’s functionality say it’s becoming increasingly challenging to find bugs in code without machine assistance. And with debugging being an arduous, time-intensive endeavor, developers are increasingly turning to code search tools, linters, and static and dynamic analysis tools to analyze millions of lines of code, flag errors and suggest fixes. 

    Whether the tool excels at bug localization, code visualization or “error” detection, entirely too many tools are insufficient when it comes to actually identify the specific lines of code that require attention. Program analysis tools only illustrate code in ways that developers still have to interpret (potentially incorrectly) and draw their own conclusions. While code search tools might speed the rate at which developers can build a mental model of the code, such tools are notorious for false positives, and developers still have to undergo the mentally challenging endeavor of piecing that mental model together to identify the code, and then safely make changes. Worse yet, code completion tools—tools that less-experienced developers disproportionately rely on—can actually propose imprecise changes (one prominent tool offers incorrect code 71% of the time), and that introduces risk.

    The cherry on top of this problematic sundae is that today’s tools don’t have any way to effectively validate the scope and accuracy of a proposed change. The influence of a suggested change can be tough to identify, so testing for comprehensive validation is frequently next to impossible. 

    Ideally, there has to be a better way to mitigate risk while making changes to legacy systems. Enter artificial intelligence. By employing a unique approach to AI, the code repository truly becomes a knowledge repository, enabling many capabilities unrealized before now. 

    A Developer’s AI-Enabled Accessible Knowledge Repository 

    By leveraging AI to automate the identification of the specific lines of code that require attention, developers can simply ask this AI-driven knowledge repository where behaviors are coming from—and quickly identify the code associated with that behavior. This puts AI squarely in the position of intelligence augmentation, which is key to leveraging its capabilities. 

    This novel approach of AI reinterprets what the computation represents and converts it into concepts, therefore “thinking” about the code in the same way humans do. The result is that software developers no longer have to unearth the intent of previous developers encoded in the software to find potential bugs. Even better, developers are able to overcome the inadequacies of automated testing by using AI to validate that they haven’t broken the system before they compile or check in the code. The AI will forward simulate the change and determine whether it’s isolated to the behavior under change. The result is the bounds of the change are confined to the behavior under change so that no unintended consequences arise.

    While AI is not yet advanced enough to safely make changes on its own, it can serve as a knowledge repository with the added capability of directing developers precisely where to make any needed changes. With AI, programmers—even on their very first day on the job — can make any necessary fixes with confidence that they won’t break the entire system. 

    Unlike its code search, code navigation, code completion and program analysis tool counterparts, AI fundamentally empowers developers to safely, efficiently and effectively make any necessary changes. With AI — that is fortified by a knowledge repository that understands source code the same way a human does — developers can truly do more with less. 

  • AI Reimagines Computation and Improves Software Development

    AI Reimagines Computation and Improves Software Development

    While modern business software solutions unlock organizational efficiencies, agility and the ability to deliver new value to customers, such tools were not available when applications deployed on mainframe systems were being developed.

    Today, many of the Fortune 500 companies, the world’s largest banks and insurance companies and several of the largest retailers in the U.S. continue to rely on legacy programming languages, such as COBOL or Java, to power their applications. However, with new languages coming on the scene and experienced programmers in legacy languages exiting the workforce, organizations find themselves in an increasingly difficult spot.

    Is it a Coding Skills Shortage or a Knowledge Gap?

    As proficient programmers retire or move on, organizations across industries are recognizing they no longer have the specialized domain knowledge of the industry and institution to effectively maintain and support their complex critical systems. To combat what is perceived as a skills shortage, organizations are sinking a significant amount of effort into hiring new developers, teaching them to write in the legacy languages and tasking them with changing enterprise systems.

    Modernizing applications, databases and systems that run well in their current state can be risky and expensive, so building on top of and around existing code can be the better choice. But such workarounds are not always completed in the most appropriate manner. For example, when a developer is cutting and pasting large chunks of code just to change one behavior, the end result is often awkward and unmanageable applications that even the most accomplished developers struggle to deconstruct and understand.

    Understanding the code is not enough. Developers have to know what an application actually does, but if they don’t recognize how altering code in one part of a system impacts another part, the consequences can be devastating. As such, the challenge is not a skills problem — most programming languages are straightforward enough to learn — it’s a knowledge problem that exists across programming languages.

    Goodbye, Years of Language-Specific Experience. Hello, AI.

    The programmers who wrote all those billions of lines of code are still running mission-critical applications, but they are increasingly aging out of the workforce or opting for a new or different career path. This results in legacy systems being less well-understood by the companies that rely on them every day. While many programming languages are meant to be easy to read and understand, unknotting the program logic frequently devolves into a tedious reverse-engineering project in which developers are attempting to unearth the intent of previous developers encoded in the software.

    Rather than relying on aging subject-matter experts to pass along that specialized knowledge to programmers fresh out of college (who are in short supply anyway), organizations might consider leveraging advanced artificial intelligence (AI) tools to automate the process of identifying the specific code that requires attention — no matter how entangled that code is throughout the system.

    While AI can be used to help with the syntactical issues of writing code (much like the Smart Compose feature leverages machine learning to “look ahead” and offer suggestions as you type), the impact of such tools is minimal. AI’s greatest time-saving opportunity lies in automating developers’ steps when conceptualizing and writing code. Whatever the reason — be it poor documentation or spaghetti code — developers spend about 75% of their time in search of the source code that needs to be changed. That source code is likely to be interspersed throughout the code repository, requiring a developer to “think through” what the code does, at nearly every step, to trace out the code detailing the behavior that they seek to modify.

    How AI Overcomes the Inadequacies of Automated Testing

    The goal of many organizations is making changes to systems running older programming languages as safely and efficiently as possible. Some organizations are turning to DevOps to improve agility and accuracy without sacrificing their existing mainframe system’s stability and performance. When it comes to COBOL code, where the problem is more acute than in other languages, automated testing in the CI/CD pipeline is inherently inadequate.

    Once a developer has identified the code representing a behavior that needs to be changed, they’re in a position, in that automated pipeline, to ensure that the corrected behavior never changes. However, because the code describing the behavior in question can be spread throughout the system, a developer will make a change believing they haven’t altered any other behavior, but they may have actually ‘broken the world.’ System-breaking changes can be made to the code -and stay hidden – because testing may not cover scenarios that the developer didn’t consider. Well-constructed automated test suites are data-dependent, but assembling all of the data necessary to get 100% code coverage is, unfortunately, next to impossible.

    If, for example, the application required a security or regulatory change, a developer might think that their correction will be covered by the automated test suite when it’s not. Without copious data in the suite, developers cannot guarantee that a behavior will never be altered. With AI “reimagining” computation as conceptual, developers are able to validate that they haven’t broken the system before they compile or check-in the code — or even run it in debug mode to check it — before running the automated test suite. Once developers are confident their change is isolated, the “conceptual check” of the behavior may be leveraged in the CI/CD pipeline, thereby ensuring the behavior remains intact from that point forward.

    In short, advanced AI makes developers’ lives easier because AI can automatically analyze source code and understand the human intent behind it. Now, developers can collaborate with AI to locate source code in an effortless manner, quickly perform comprehensive impact analysis and make the necessary change(s) without ‘breaking the world.’ The improved productivity and efficiency AI unlocks is critical in maintaining and improving the important legacy and mainframe systems that handle tens of billions of business transactions every single day.