An AI alternative to code search tools

October 4, 2022

Would you believe that the average software developer spends roughly 75% of their time just searching through and understanding code to make necessary changes? When software engineers have to spend so much of their time just finding and understanding legacy code, before any real work gets done, they have less time to create new solutions to move an organization forward.

Phase Change President Steve Brothers recently penned an article for the Infoworld New Tech Forum titled, "An AI alternative to code search tools," about how AI tools are becoming available to close the application knowledge gap for developers, promising to exponentially improve developer productivity across applications. Specifically, Brothers wrote about Phase Change's COBOL Colleague, an AI-driven tool that helps developers quickly gain a mental model of a COBOL codebase, and zero in on the exact code they need to change.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Applied, Technology

Tags: AI, artificial intelligence, cobol colleague, software developer, software development, software maintenance, steve brothers

Colleague is a task-oriented tool that identifies the code that needs to be changed and helps with that change

October 4, 2022

When organizations must make source code changes or migrate applications to alternative platforms, they frequently understand what the code does. Often, the people who wrote the code have departed the organization and someone has to learn a great deal about the code to determine which code matters. This lack of application knowledge introduces significant risk to the organization.

Phase Change President Steve Brothers was recently interviewed by the devmio blog to talk about COBOL Colleague, Phase Change's upcoming product release, which assists developers in focusing on the relevant code for required source changes. In the article, "Colleague is a task-oriented tool that identifies the code that needs to be changed and helps with that change," Brothers talked about how developers can describe the application behaviour to Colleague's AI agent, and it returns only the execution-order code and requisite data needed to reproduce the behaviour.

Brothers also talked about the future of AI in software development and Phase Change's plans for its technology moving forward.

Read the entire interview here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Applied, Technology

Tags: cobol colleague, legacy systems, software developer, software maintenance, steve brothers

Solving the issues with current documentation practices

September 1, 2022

Software development is typically a team endeavor. Developers may work on separate projects but many times their work intersects with modules others are building. Even individuals creating their own applications must refer back to prior work to track source-code changes and limit vulnerabilities. Creating proper documentation for teamwork and legacy code should be a top priority for all developers.

The consequences of missing or inadequate documentation impede application updates and new feature additions, or worse, affect end users by delivering buggy products or missed delivery deadlines.

Phase Change President Steve Brothers was recently interviewed for an article published by SD Times titled, "Solving the issues with current documentation practices," about how software development and maintenance documentation remains an issue. In the interview, Brothers said many times documentation is not a priority because of time constraints – developers feel they are paid and assessed on the code they create, not on documenting the process. And when they do provide comments, once again, project constraints can lead to inaccurate information. This failure to transfer knowledge leads to "slower and sloppier development."

Brothers also talked about coming AI tools that will automatically capture the knowledge developers put into the code, thus creating its own documentation, which never leaves the organization, even when the developers depart. Phase Change's AI tool, COBOL Colleague, will also help automate the process of searching for relevant code and data, which minimizes the need for extensive documentation.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Applied, Community, Technology

Tags: AI, artificial intelligence, developer productivity, documentation, productivity, sd times, software developer, software development, steve brothers

Combining developer knowledge with artificial intelligence to improve software maintenance

May 19, 2022

Enterprise software systems are complex and require specialized abilities and unique knowledge to update, add new features, and generally solve problems. They necessitate ongoing systems maintenance to grow and evolve, which costs your organization a significant amount of money – generally about three-quarters of your IT software budget. Unfortunately, because of the global software developer shortage, the typically brief developer average tenure at one job, and today’s inadequate source-code search tools, linters, and static and dynamic analysis tools, organizations across industries are struggling to maintain their software systems effectively.

Phase Change President Steve Brothers recently wrote an article for The Next Tech about how a novel approach to artificial intelligence (AI) software tools can help enterprises save a significant amount of time and money while minimizing the risks associated with making changes in complex software systems. The article, "Combining developer knowledge with artificial intelligence to improve software maintenance," discusses how AI and cognitive automation can automate the identification of the specific lines of code that require attention — no matter how entwined throughout the system that code might be – at machine speed. The tools also comprehend and reveal all of the upstream and downstream changes that will occur due to code modifications so developers can be confident when updating source code to add new features, fix bugs, meet regulatory requirements, and address information security concerns.

Read the entire article here.

Published by Phase Change Staff in Applied, Technology

Tags: AI, application maintenance, artificial intelligence, developer productivity, developer-engineer, enterprise software, enterprise software maintenance, industry turnover, software developer, software development, software maintenance

Improving developer productivity on the mainframe with artificial intelligence

March 3, 2022

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 [email protected].

Published by Phase Change Staff in Applied, Technology

Tags: AI, artificial intelligence, assistive AI, developer productivity, developer-engineer, legacy systems, mainframes, maintaining legacy systems, productivity, software developer, software development, software maintenance, steve brothers

You can use artificial intelligence to fix your broken code

February 23, 2022

Mainframe systems are used across industries and around the globe, with over 10,000 currently in worldwide use. They are relied on by some of our most important institutions, including 96 of the world’s 100 largest banks, nine out of 10 of the world's biggest insurance companies, 23 of the 25 largest U.S. retailers, and 71 percent of Fortune 500 companies. Unfortunately, often because of a lack of detailed understanding of these mainframe systems, making source-code changes can be costly, risky, and can tarnish the organizations' reputations.

Phase Change President Steve Brothers recently wrote an article for BuiltIn.com titled, "You Can Use Artificial Intelligence to Fix Your Broken Code," which explains how artificial intelligence (AI) can help developers better understand the codebase, and help them find code responsible for application behavior at machine speed. Developers will no longer have to pore over millions of lines of code to unearth the intent of previous developers and find the source code that requires change.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Applied, Technology

Tags: AI, application maintenance, artificial intelligence, assistive AI, business agility, developer productivity, developer-engineer, legacy systems, mainframes, maintaining legacy systems, software developer, software development, software maintenance, steve brothers, system knowledge

How banks should leverage the power of automation

February 16, 2022

Mainframes are widely considered the backbones of many global financial services firms because they deliver unparalleled security, stability, and processing power. From credit card payments and ATM transactions to loans and mortgages, mainframes are relied on by 44 of the top 50 banks to host core applications that deliver secure experiences based on real-time data analytics.

Phase Change President Steve Brothers recently penned an article for TechBullion.com titled, "Banking automation: How banks should leverage the power of automation," in which he examines how these critical mainframes systems also present modernization challenges.

Mainframe systems are complicated and require meticulous processes to continue providing core operational value. While they are fully capable of running newer applications and systems to create new products and revenue streams, their ongoing support and modernization are challenging.

Brothers believes automation and artificial intelligence (AI) could greatly assist banking firms in maintaining and enhancing their mainframes because the key to sustaining these systems is precisely identifying the functionality created by the source code that is intertwined throughout the system — and changing that behavior without unintended consequences. Using a new AI approach that's designed to sift through large quantities of code in the same way humans do, AI-powered tools can aid developers in their frequent search through the deluge of code to rapidly identify where they need to make a change.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Applied, Technology

Tags: AI, application maintenance, artificial intelligence, brain drain, business agility, developer productivity, developer-engineer, domain knowledge, enterprise software, enterprise software maintenance, industry turnover, legacy systems, mainframe legacy software, mainframes, maintaining legacy systems, productivity, software developer, software development, software maintenance

AI Powers the Future of Financial Services — Just Not in the Ways You Think

February 7, 2022

Phase Change President Steve Brothers was recently interviewed for an article in The Fintech Times that considers the role AI could soon play in the financial industry. The article, "Phase Change: AI Powers the Future of Financial Services — Just Not in the Ways You Think," examines how AI will help maintain the software that runs the global financial enterprises, as well as other mainframe-based industries.

AI is already utilized by financial-industry players to automate investments, insurance, trading, banking services, and risk management, primarily on mainframes originally developed in the 1960s. Mainframe computing systems provide high security; high-speed, high-volume transaction processing; and reliable uptime. However, they can be complicated to use and require constant maintenance. Plus, they struggle to evolve quickly enough to support the increasing number of banking services supported by cloud mobility and big data.

New AI technologies can soon be used to automate software maintenance by helping developers better comprehend the source code — and make changes rapidly and precisely. The programmers that developed and maintained these huge and complex systems are in high demand (and are paid like it) or aging out of the workforce, and the financial institutions that rely on them are scrambling to understand the codebases with less experienced developers.

Rather than relying on knowledge transfer protocols to pass along specialized domain and program knowledge, financial institutions can now deploy advanced AI-powered tools to automate the process of identifying specific code that requires attention, regardless of how entangled that code is throughout the system.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Applied, Technology

Tags: AI, application maintenance, artificial intelligence, business agility, developer-engineer, domain knowledge, industry turnover, knowledge gap, legacy systems, mainframes, maintaining legacy systems, software developer, software maintenance, steve brothers, system knowledge

How AI can improve software development

January 25, 2022

Transforming business operations is a constant need, and the pandemic-prompted emphasis on modernizing legacy computing systems has forced organizations across industries to accelerate their modernization plans. 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 improving and even simply maintaining legacy systems.

Phase Change President Steve Brothers recently authored a contributed article for DevOps.com about how artificial intelligence (AI) tools can help developers work more productively and decrease the risks associated with legacy system modernization and maintenance.

The article, "How AI Can Improve Software Development," explains how today's bug localization, code visualization, and error detection tools don't actually identify specific lines of code that require change. And, once the code is identified, developers are still required to build mental models of their applications to make sure any source code changes don't make even more bugs or crash the entire system.

Through intelligence augmentation, AI can automate the identification of specific lines of code that require change – developers simply ask the AI-driven knowledge repository where unwanted behaviors are coming from, and the AI quickly identifies the code associated with that behavior. Also, before the developers compile or check in the new code, the AI can forward simulate the changes and validate that they won't create more problems or break the system.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Applied, Community, Science, Technology

Tags: AI, application maintenance, artificial intelligence, assistive AI, brain drain, developer productivity, legacy systems, mainframes, maintaining legacy systems, software developer, software maintenance, steve brothers

Leveraging AI to Significantly Increase Software Developer Productivity

January 10, 2022

Tech media publisher readwrite recently published an article authored by Phase Change President Steve Brothers about how AI can be used to vastly improve a developer’s ability to efficiently identify code that requires modification or modernization.

The article, Leveraging AI to Significantly Increase Software Developer Productivity, makes the case for thinking about codebases differently and using AI to help developers quickly and efficiently find relevant code.as knowledge repositories.

Developers new to software applications often require months or even years of on-the-job training to avoid making dangerous mistakes and putting critical systems at risk. With today's tools, developers spend roughly 75% of their time searching through and reading source code to identify the relevant code that produces the functionality that requires modification or modernization.

By using AI tools to analyze source code and discover each and every one of its behaviors at machine speed, the code repository can become a knowledge repository that represents source code in the same way that humans think about the world, in cause and effect. The AI interacts and collaborates with developers to disregard code unrelated to the behavior and narrows down the codebase to the specific code that needs to change, without searching through and understanding all of the surrounding code.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

Published by Phase Change Staff in Community, Technology

Tags: AI, application maintenance, artificial intelligence, assistive AI, developer productivity, new developer, software developer, steve brothers

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