Mainframes are the central data repository in an organization’s data processing center. They support thousands of applications and input/output devices while simultaneously serving thousands of users. Most corporate data still lives on the mainframe, and these systems offer advanced capabilities, flexibility, security, and resilience to downtime. Unfortunately, mainframe management and modernization can be costly, risky, and can damage an organization’s reputation by crashing internal and customer-facing applications if developers don't know the system.
Phase Change President Steve Brothers recently authored an article for Techslang.com titled, "Improving Developer Productivity on the Mainframe with Artificial Intelligence," which discusses the roles mainframes play in multiple industries including finance, healthcare, and government, and the difficulties reliant organizations face maintaining and integrating them with modern tools.
To maintain and improve critical mainframe applications, software teams rely on seasoned developers who have developed an intimate understanding of their systems. Unfortunately, many of these experienced programmers are aging out of the workforce or opting for other opportunities – creating a loss of knowledge about those organizations' mainframe applications.
In the article, Brothers explains how AI can automate the process of precisely and accurately identifying code that requires attention — no matter how dispersed throughout the system it might be. By guiding these AI tools through describing the application behavior that needs to change, developers don’t have to search through and develop an intimate understanding of, massive source code bases to reveal the specific lines implementing that behavior. They can now collaborate with an artificially intelligent coworker to augment their own intelligence and be guided exactly to the code that matters.
Read the entire article here.
Todd Erickson is a Technology Writer with Phase Change. You can reach him at terickson@phasechange.ai.
October 25, 2022
How a Novel Approach to AI Mitigates the Need for Comments in Code
Code comments are often difficult to understand, incomplete, out of date and untrustworthy to many developers, resulting in significant additional work and unintended business risks. Incorrect documentation results in time and money lost. Transitioning away from relying on developers to add and update comments in code and related documentation requires new methods and tools.
Steve Brothers, President of Phase Change Software, recently addressed this challenge in his article: "How a Novel Approach to AI Mitigates the Need for Comments in Code." He explains how new AI technology can exponentially improve software development productivity by assisting new developers with identifying code behavior and locating the exact place in the code where changes are needed.
Stephen Tullos is an Analyst with Phase Change Software. You can reach him at stullos@phasechange.ai.