mainframes Archives - Phase Change Software

April 11, 2022

Reputational Risk: How AI Helps Mitigate Damage to Your Brand

When maintenance issues result in mission-critical application downtime or crashes, your organization will likely lose market share, social capital, and maybe most important – reputational risk. A 2019 IBM report revealed that 41% of IT leaders surveyed indicated that the costliest aspect of downtime is its negative impact on corporate reputation.

Phase Change President Steve Brothers recently authored an article for CEOWORLD magazine titled, "Reputational risk: How AI helps mitigate damage to your brand," about how artificial intelligence (AI) can now be used to locate specific code that's causing maintenance issues (and downtime) to improve developer productivity and ensure that source code changes remain intact and won't cause more problems down the road.

Read the entire article here.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at terickson@phasechange.ai.

Published by Phase Change Staff in Applied, Technology

Tags: AI, application knowledge gap, application maintenance, artificial intelligence, developer productivity, mainframes, reputational risk, software maintenance, steve brothers

March 3, 2022

Improving developer productivity on the mainframe with artificial intelligence

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.

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

February 23, 2022

You can use artificial intelligence to fix your broken code

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 terickson@phasechange.ai.

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

February 16, 2022

How banks should leverage the power of automation

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 terickson@phasechange.ai.

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

February 7, 2022

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

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 terickson@phasechange.ai.

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

January 25, 2022

How AI can improve software development

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 terickson@phasechange.ai.

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

January 12, 2022

Phase Change Published Articles

The continuing departure of experienced mainframe legacy software engineers from the workforce is driving the potentially devastating lack of system knowledge and expertise now confronting businesses and governments around the world. These mainframes surreptitiously run the global building blocks of society, from government systems to banking and financial markets and healthcare and insurance industries.

Phase Change Software endeavors to engage the industry in conversations about AI's role in bridging the knowledge gap by delivering computation conceptualization and impact verification at machine speed that produces radical productivity improvements.

We've collected our published industry articles and interviews here for your convenience. To continue the conversation, please contact Steve Brothers, President of Phase Change Software.

How a Novel Approach to AI Mitigates the Need for Comments in Code
by Steve Brothers
October 14, 2022
TechNative

How COBOL Code Can Benefit from Machine Learning Insight
by Steve Brothers
October 21, 2022
The New Stack

Colleague is a task-oriented tool that identifies the code that needs to be changed and helps with that change
by Janine Jochum-Frenster
September 29, 2022
devmio blog

An AI alternative to code search tools
by Steve Brothers
September 6, 2022
Infoworld New Tech Forum

Solving the issues with current documentation practices (interview)
by Katie Dee
August 1, 2022
SD Times

Combining developer knowledge with artificial intelligence to improve software maintenance
by Steve Brothers
May 12, 2022
The Next Tech

Reputational Risk: How AI Helps Mitigate Damage to Your Brand
by Steve Brothers
April 7, 2022
CEOWORLD magazine

Improving developer productivity on the mainframe with artificial intelligence
by Steve Brothers
February 22, 2022
Techslang.com

You can use artificial intelligence to fix your broken code
by Steve Brothers
February 22, 2022
BuiltIn.com

How banks should leverage the power of automation
by Steve Brothers
February 9, 2022
TechBullion.com

Phase ChangeAI Powers the Future of Financial Services — Just Not in the Ways You Think
by The Fintech Times
February 1, 2022
The Fintech Times

How AI can improve software development
by Steve Brothers
January 13, 2022
DevOps.com

Leveraging AI to Significantly Increase Software Developer Productivity
by Steve Brothers
December 13, 2021
readwrite

How AI can support maintenance of aging government systems
by Steve Brothers
July 20, 2021
Nextgov.com

AI rises to the challenge with COBOL
by Steve Brothers
May 28, 2021
techradar.pro

Leveraging AI to close the application knowledge gap
by Steve Brothers
May 19, 2021
BetaNews.com

Can AI solve the engineer shortage?
by Steve Brothers
May 15, 2020
ColoradoBiz Magazine.com

Published by Phase Change Staff in Community, Company

Tags: AI, artificial intelligence, cobol skills shortage, mainframes, steve brothers

July 20, 2021

IEEE conference accepts paper co-authored by Phase Change scientists

The International Conference on Software Maintenance and Evolution (ICSME) 2021 accepted a technical paper authored by current and former Phase Change research scientists for presentation at its 37th annual event in Luxembourg City, Great Duchy of Luxembourg, September 27 - October 1.

The paper, "Contemporary COBOL: Developers' Perspectives on Defects and Defect Location," was co-authored by current Phase Change Senior Research Scientist Rahul Pandita, former Senior Research Scientist Aleksander Chakarov, and former intern Agnieszka Ciborowska.

The authors' goal is to direct the attention of researchers and practitioners towards investigating and addressing challenges associated with mainframe software development. More specifically, they present results from surveys of COBOL and more modern programming languages regarding defects and defect-location strategies. Software development has made substantial advances in software maintenance for modern programming languages but mainframe programming languages receive limited attention.

Meanwhile, mainframe systems are facing a critical shortage of experienced developers as the current generation retires. Without extensive mainframe and application-specific experience, replacement developers face significant difficulties, even during routine maintenance tasks such as code comprehension and defect location.

ICSME is an annual event sponsored by the Institute of Electrical and Electronics Engineers (IEEE) to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution. This year's conference will be a virtual event.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at terickson@phasechange.ai.

Published by Phase Change Staff in Company, Technology

Tags: cobol, cobol applications, cobol brain drain, cobol skills shortage, conferences, icsme, legacy systems, mainframes, maintaining legacy systems, software developer application knowledge gap, software maintenance, technical paper

April 9, 2021

Phase Change President: Creative & focused AI needed to help COBOL skills shortage

The so-called "COBOL Skills Shortage" is compelling many organizations to impetuously hire and train programmers to maintain, support, and attempt to modernize their COBOL systems.
But understanding how to write COBOL is not enough — developers have to comprehend what an application actually does and how code changes can impact the system as a whole to avoid critical missteps. That work for those developers is cognitively difficult.

Phase Change President Steve Brothers recently wrote an article for Built In Colorado.com about how artificial intelligence (AI) can help solve the application knowledge gap problem, but only when traditional AI technology gets more creative and moves beyond understanding general business knowledge and instead learns specialized industry and institutional domain knowledge.

AI & software development

AI can help solve the application knowledge gap dilemma, but popular contemporary AI approaches are insufficient. Some AI tools can help with the syntax of writing code, but these remedies only provide incremental value.

Developers spend nearly 75 percent of their time finding the area in the source code in which they need to make a change because understanding code in these large complex systems is difficult and time-consuming.

AI will emerge as a paradigm-changing technology when it can understand code intent and “reimagine” computation into concepts, thereby doing what a developer does when they code — but at machine speed.

Read Steve’s entire Built In Colorado article at https://builtin.com/artificial-intelligence/cobol-skills-shortage.

Published by Phase Change Staff in Applied, Company, Technology

Tags: AI, application knowledge gap, artificial intelligence, assistive AI, cobol, cobol brain drain, developer-engineer, executives, knowledge gap, mainframes, maintaining legacy systems, software development, steve brothers, understanding

October 23, 2020

Modernize your mainframe instead of migrating away for higher customer and company satisfaction says IDC study

October 23, 2020

by Todd Erickson

Modernizing your IBM mainframe instead of migrating off the platform leads to higher customer and company satisfaction says and IDC study of 440 organizations in Australia, India, New Zealand, the United Kingdom, and the United States.

Commissioned by Rocket Software, a legacy infrastructure consulting firm, the study as reported by IT Jungle found that IBM shops that modernized their IBM mainframe infrastructure instead of migrating to more modern platforms were more satisfied across a number of metrics before and after the projects.

The shops that modernized reported higher satisfaction than the shops than migrated across the following 7 metrics:

(1) customer experience;
(2) overall performance;
(3) security, availability, and disaster recovery capabilities;
(4) agility, microservices, and DevOps;
(5) ease of finding talent;
(6) ability to incorporate AI and IoT;
(7) and API, mobile, and Web enablement.

According to the study report, in addition to seeing higher satisfaction ratings, the organizations that modernized generally reported paying less on hardware, software, and staffing.

The study seems to dispel the common IT industry myth that mainframe platforms are less capable than more modern systems simply because of their age.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at terickson@phasechange.ai.

Published by Elizabeth Richards in Applied

Tags: IBM, mainframes, maintaining legacy systems, modernization

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