Phase Change executive quoted in ‘COBOL skills shortage’ article

January 22, 2021

Phase Change COO Steve Brothers was interviewed and quoted in a TechRadar Pro article published on December 18 about the 'COBOL skills shortage.' He shared his insights on how 'knowledge attrition' – an organization's declining application knowledge due to the departure of experienced software developers – was really the cause of government system failures during the COVID-19 pandemic, and why it remains a serious problem today.

Legacy applications and the COBOL skills shortage were widely blamed for government financial-aid system failures during the first few months of the Coronavirus pandemic. But the TechRadar Pro article revealed that the system failures were not a result of the lack of COBOL programmers. The problem was a severe shortage of legacy-application programmers that understand how these legendary applications work and what the source code does.

“In the COBOL space, you have millions of lines of active code and, to perform necessary maintenance, you need developers that understand what that code does," Brothers said. "But when you’re writing complex applications, code written in the morning becomes legacy by the afternoon.”

The story describes Phase Change's initial market product, COBOL Colleague, which is currently in beta testing and scheduled for release in Q2, and how it is designed to collaborate with developers new to legacy applications and make it easier for them to complete maintenance tasks without requiring experienced colleagues or subject matter experts.

Read more about the 'COBOL knowledge attrition problem' facing government and large financial systems in TechRadar Pro's December 18 article, "We're all at the mercy of this decades-old programming language, but we’ve been thinking about it all wrong."

Phase Change granted fourth U.S. patent

January 22, 2021

Phase Change was recently issued the fourth patent by the U.S. Patent and Trademark Office (USPTO) related to its ground-breaking software-development technology. The company's first patent was granted in May 2019, and subsequent patents were issued in October and December of the same year. This fourth patent is scheduled to be issued on December 29, 2020.

The first patent is based on the consideration that one function or specification can be implemented in many different ways. This patent provides a method to automatically replace a snippet of code with another snippet of code if these are determined to be strictly equivalent. Using a logical analysis of the two functions, our tool can determine if they are equivalent or not. If they are equivalent, the snippet of code is automatically replaced by the new one, provided that it improves the overall program in some way.

Phase Change's second patent is built upon the first and focuses on improving readability and maintainability. This patent is based on the consideration that the source code of many programs today suffer from a lack of readability (e.g. spaghetti code including “GO TO” statements) and/or maintainability (e.g. legacy code). Using the same logical mechanism as the first patent, this invention will replace a snippet of code with another equivalent snippet of code that has been previously identified as better with respect to readability and/or maintainability.

The recent patent is also built upon the first patent and focuses on security considerations. It is based on the consideration that the source code of many programs today may not have sufficient security components to protect the applications from wrongful and intrusive attempts, such as hacking and piracy efforts. Using the same logical mechanism as the first patent, this invention will replace a snippet of code with another equivalent snippet of code that has been previously identified as better with respect to security.

Phase change was granted another foundational patent in December 2019. This invention normalizes the source code into a language-agnostic representation called Dependency-Ordered Behavior (DOB), a representation that doesn’t depend on the specificity of the programing language (e.g. Java, C, or COBOL), but solely on the behavior of the application. Once the source code is normalized, this tool can easily extract paths within the application and associate these paths with semantic names. Combinations of paths can also automatically create combinations of semantic names.

Phase Change currently has 13 active patent applications in four countries. For more information on Phase Change’s patent portfolio, email [email protected].

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

Application knowledge is the foremost skill developers need for rapid legacy-system success

October 27, 2020

October 27, 2020

by Todd Erickson

The COVID pandemic has highlighted the importance of maintaining legacy computing systems, and the need for more mainframe software developers.

But as Bill Hinshaw, owner of COBOL Cowboys, says in the latest Phase Change podcast, learning a "legendary" mainframe language such as COBOL is only about 10% of getting developers productive in applications they don’t understand.

Learning COBOL is probably about 10% of getting a person productive. It's the other 90% learning about that organization, how they handle business rules.

The other 90% is educating developers on how the applications run and what business rules the organizations have embedded in the applications.

Bill and Eileen Hinshaw founded COBOL Cowboys to provide software development and support for legacy environments. Bill has nearly 60 year of experience in mainframe software development and IT.

In this Phase Change podcast, Bill points out that while many organizations have announced plans to move away from legacy applications to newer technology, they won't be able to quickly rip-and-replace the 200 billion-plus lines of COBOL code currently in use.

Bill says that one reason why we won't see widespread replacement of COBOL-based applications is because a lot of companies lost the expert developers that built and understand the legacy systems through the years.

He explains why public and private organizations are struggling to find people willing and able to maintain applications written in code developed decades earlier, and how programmers that are new to legendary applications often take months to learn the systems before they are productive.

The podcast also includes our conversation about the coming role of AI in software development and how critical it will be for helping developers become more productive with mainframe systems more quickly.

Learn more about the COBOL Cowboys and how critical mainframe applications are to the world in this Phase Change podcast.

 

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

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

October 23, 2020

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 [email protected].

An Analogy: Software AI and Natural Language — blog

March 6, 2017

March 6, 2017

Today's AI technology is amazing.

Only a few short years ago, only humans could interpret the meaning of text and speech. Now our cell phones understand our voices and language well enough to distinguish accents, metaphors, and sarcasm.

IBM's Watson supercomputer even understood Alex Trebek well enough to beat some of Jeopardy!'s® best players.

Computers achieve natural-language understanding through a series of logically consistent normalization steps -- starting with the processing of basic sounds to recognizing words and then understanding sentences.

If computers can understand natural language using logically consistent processes, shouldn't we be able to use similar processes to break down and normalize software?

In fact, shouldn't software be easier to normalize than the messy ambiguity of human communication?

The answer is yes.

Phase Change normalizes software source code into formal data types and organizes them into hierarchical structures that are probabilistically linked (horizontally and vertically). Our technology unlocks the vast domain and system knowledge embedded in software and makes it available to anyone involved in creating and supporting software.

To learn more about how Phase Change's revolutionary technology transforms chaotic code into coherent data and intractable software into artificially intelligent agents, read Steve Bucuvalas' paper: "An Analogy: Software AI and Natural Language."

Leveraging software’s encoded knowledge to create an assistive AI — science podcast 4 of 4

February 16, 2017

February 16, 2017

This is the fourth and final in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

Using a whimsical example of dog banking, Steve discusses how the knowledge that’s encoded in software is normalized into a data structure, which enables us to create an assistive AI and solve the learning curve problem.

Podcast Slides and References

Time Stamps Slides and References
00:11 Steve Bucuvalas Podcast – Equality: The fundamental operation for software as data -- science podcast 3 of 4
05:15 PowerPoint Slide #1: Black-box view of Dog banking application -- the user (dog) view
05:21 PowerPoint Slide #2: White-box view of Dog Banking application -- the developer view
08:30 PowerPoint Slide #3: Merging the black-box and white-box views -- Dog Banking source code sliced into functional segments

Equality: The fundamental operation for software as data — science podcast 3 of 4

February 16, 2017

February 16, 2017

This is the third in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

In this podcast, Steve addresses the fundamental operation for software to be treated as data, which is equality, and begins by asking how we know when a fundamental unit of software is equal to something else? The first talk in this series introduces the idea of compiling programs into an AI representation. In the second talk, the Turing and Rice proofs are shown that they only apply to the mental domain of computation.

Podcast Slides and References

Time Stamps Slides and References
00:28 Steve Bucuvalas Podcast – Changing the essence of software and creating breakaway efficiency — science podcast 1 of 4
00:36 Steve Bucuvalas Podcast – The Turing machine, the Halting problem, and Rice’s use of the Turing proof — science podcast 2 of 4
02:50 PowerPoint Slide #1: Using C-language functions to show functional equivalence determination method
09:05 PowerPoint Slide #2: Stack Overflow thread about Turing's Halting problem -- Online Thread
10:34 Steve Bucuvalas Podcast – Leveraging software’s encoded knowledge to create an assistive AI — science podcast 4 of 4

The Turing machine, the Halting problem, and Rice’s use of the Turing proof — science podcast 2 of 4

February 16, 2017

February 16, 2017

This is the second in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

Steve reviews Turing's Halting problem and Rice's theorem, which have influenced computational theory for years. He shows how their abstract theories about infinity and an infinite number of programs do not apply to finite software programs in the real world.

Changing the essence of software and creating breakaway efficiency — science podcast 1 of 4

February 16, 2017

February 16, 2017

This is the first in a series of practical talks by founder and CEO Steve Bucuvalas about Phase Change Software, what we are developing, the math and science behind our technology, and the impact on the software development process.

In keeping with the physics' definition of the term ‘phase change,’ we are changing the essence of software. Taking something that is chaotic and turning it into something coherent. Taking something that is intractable and hard to understand and making it into an AI that actively helps every person in the software development process.

How Phase Change’s AI impacts release management — video

January 5, 2017

January 5, 2017

Phase Change President Gary Brach leads a practical discussion with Ken Hei, director of engineering, and Brad Cleavenger, senior software architect, about how Phase Change's technology will transform release management.

 

Contact

Phase Change Software
13949 W. Colfax Ave
Building 1, Suite 205
Lakewood, Colorado 80401
Phone: +1.303.586.8900
Email: [email protected]

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