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.

February 24, 2021

Legacy system failures expose the application knowledge gap’s harmful risks

February 24, 2021

by Steve Brothers

Government system failures during the rush to provide public benefits to alleviate the economic effects of the COVID-19 pandemic publicly exposed the mainframe knowledge crisis that also threatens financial institutions, healthcare providers, and many other organizations foundational to the world economy.

Several states discovered the knowledge-gap’s potentially devastating consequences when waves of unemployment-claims poured into their systems as COVID-19 ravaged the economy in early 2020. The states’ unemployment computer systems crashed trying to process the deluge of claims using mainframes and decades-old programming languages.

But it wasn’t the mainframes or the legacy programming languages that failed, despite what you may have read. It was the lack of available expert programmers necessary to maintain and update these systems to handle the voluminous claims.

According to The Verge’s article, “Unemployment checks are being held up by a coding language almost nobody knows,” Colorado employed exactly one full-time programmer to maintain the state’s COBOL system prior to the pandemic. Back then, Colorado processed roughly 2,000 unemployment claims per week. In March and April 2020, that number rocketed to as high as 104,572 claims per week.

Now governments, non-profits, and private organizations are reviewing their systems’ strategies to learn from these mistakes. If your business relies on legacy systems, you probably should keep reading – and schedule some time with your IT people.

Mainframes are cornerstones

Legacy mainframe systems and software bedrock many of our most trusted institutions, including government services, finance and banking, healthcare, and insurance. In a substantial number of cases the expert developers that created and maintained these systems and software are retiring without a supporting workforce to replace them.

Besides the people that make your business run, your software is potentially the most important resource your organization has. Internal applications likely drive your employees' capabilities and productivity. Customer-facing programs attract new customers, close business deals, and increase revenue. New applications and features can open new markets and opportunities.

To maintain and improve your critical applications, your software team relies on individual engineers that developed an expert understanding of your programs through years of experience. They know the applications and all the accumulated system changes and challenges.

When those experienced engineers depart your business, the developers that replace them must acquire the same application knowledge through training, mentorship, and on-the-job programming. This exercise introduces several material business risks.

Learning on the job

Developers new to software applications typically require 6-12 months of on-the-job learning to become productive, depending on the size of the source code base. To become proficient, programmers may need up to 3 years.

Without qualified software developers knowledgeable about your applications, you endanger your business's operations, reputation, and security. You also risk a significant decrease in your software teams' productivity and efficiency.

Consider the monumental task confronting newly hired or transferred IRS developers last March. Congress passed the CARES Act on March 25, 2020, and then Treasury Secretary Steven Mnuchin announced that individual stimulus checks would be mailed in early April.

To assist with the delivery of economic stimulus payments, the new developers were required to immediately start working with the agency's source code base, which, in 2019, was estimated at nearly 20 million lines of code and includes over 60 years of legislative and system changes. As of mid-May 2020, nearly 20 million people had not received their stimulus checks, and some recipients had problems throughout the year.

These engineers didn’t have 6-12 months to become productive. They had to hit the ground running on day one. And without the benefit of weeks or months of training and on-the-job learning, they didn’t have the application knowledge necessary to understand how even simple changes could affect entire applications.

And let's not forget the productivity loss due to the remaining IRS's experienced engineers for training, mentoring, and supervising the new recruits.

What’s your risk?

Your situation may not be as dire as what the IRS faced – for now. But how much time can you really give your new developers to learn the system, and how much productivity can you afford to lose while your experienced programmers train and supervise them?

How much do you trust the developers that have just started working on your critical applications?

How confident will you be when your CEO or Board of Directors asks for assurances that the next customer-facing application update will not result in outages and lost revenue – especially if the update was programmed by a developer you hired just weeks ago?

Your software is a critical part of your organization, especially if you rely on legacy mainframe systems. You must have a plan or tool that keeps the code running and bridges the gap between retiring and departing developers and the people that will replace them.

Steve Brothers is the President of Phase Change Software. You can reach him on LinkedIn or at sbrothers@phasechange.ai.

December 11, 2019

How short-term maintenance practices can double application size in 5 years

December 11, 2019

by Todd Erickson and Elizabeth Richards

Software must evolve to stay effective, which makes application maintenance a persistent and growing obligation, especially for organizations with large, legacy systems.
Changing marketplaces, compliance updates, security patches, hardware improvements, bug fixes, and process updates all drive code changes.

In his book, Building Maintainable Software: Ten Guidelines for Future-Proof Code, Software Improvement Group Co-founder and CTO Tobias Kuipers says that in larger codebases, 15% of the source code is changed each year.

And software maintenance isn't cheap. Kuipers says that some of his clients spend up to 90% of their IT budgets on program upkeep.

A common problem is the technical debt that piles up when the software team doesn't understand the code they are modifying and its system interdependencies. Combine that lack of knowledge with time and resource demands and the team often resorts to short-sighted modification techniques that add code instead of modifying it in place, which only increases the codebase size, complexity, and technical debt.

Maintenance Challenges for Legacy Code

Legacy applications can have massive and complex code bases created by hundreds of developers throughout decades of work. For example, in 2012, The Bank of New York Mellon reported that its core banking system contained 323 million lines of code and 112,500 COBOL programs. With that size and complexity, even an experienced developer can’t know the whole system.

One issue is the lack of useful documentation. A Catholic University of Brazil study found that between 40% to 60% of maintenance activity is studying the software just to understand it, and the impact analysis required to make the changes without breaking functionality.

Updating documentation can shorten time-to-competency, but it's frequently a low-priority task when stakeholders are demanding that bug fixes, security updates, and functionality improvements be completed yesterday.

Another challenge is institutional brain drain. Inevitably, experienced developers depart the software team, and the loss of that expert knowledge extends the amount of time it takes new developers to understand the applications because there are fewer experienced colleagues they can rely on for assistance.

How do software teams cope?

Application change is required but the lack of understanding introduces risk. To decrease immediate costs and risks, developers and managers may choose to use short-sighted strategies.

Don’t touch the black box

One technique programmers use to avoid breaking unmanageable applications is building separate subsystems (Figure 1).


Just copy the whole damn thing

Another tactic is to duplicate the applicable code (Figure 2). Good development practices recommend editing code rather than duplicating it, but if developers don't understand the code they are editing or its interdependencies, they risk breaking the old functionality.

Instead, developers leave the applicable code in the application, but copy it and place the copy in a new location. Then they modify the duplicate code, hopeful that by leaving the original code in place, they won’t break its functionality.


Duplicating code reduces the risk of breaking the application in the short-term but increases maintenance costs and program complexity in the future. By adding 15% to the code base annually, it will double in just 5 years, making maintenance that much more difficult, expensive, and risky.


Conclusion

Companies face a difficult situation when they choose short-terms strategies that avoid immediate cost and risk but end up creating long-term technical debt.

The solution is to ensure that developers understand the code completely to make sound development decisions. However, the speed of business and technical change affords few organizations the time needed to completely understand their applications.

To learn more about how Phase Change's COBOL Colleague helps developers understand complex COBOL-based applications and make changes quickly and confidently, visit Phase Change's product page.

Elizabeth Richards is Phase Change's Director of Business Operations. You can reach her at erichards@phasechange.ai.
Todd Erickson is a Technology Writer with Phase Change. You can reach him at terickson@phasechange.ai.

December 19, 2018

Phase Change unveils COBOL Colleague product website

December 18, 2018

by Todd Erickson

Phase Change announces the launch of its initial product website – CodeCatalyst.ai. The website will support the company's market entry product, COBOL Colleague, the first cognitive tool for software development, by targeting organizations that rely on COBOL-based applications for critical business operations.

The CodeCatalyst.ai website details how COBOL Colleague will assist COBOL reliant organizations with their unique issues, such as a vanishing workforce, lost application knowledge, and lagging productivity.

COBOL Colleague reads-in the source code, extracts the embedded concepts, discovers the dependencies, reveals the buried knowledge, and becomes an expert that never tires and never leaves.

Natural-language-interaction enables developers and stakeholders with limited COBOL experience to collaborate with the cognitive agent and work productively with their COBOL applications.

Find bugs and dead code in seconds, not minutes or hours. Make changes with full knowledge of the downstream impact. Confidently add new features, products, and services. Empower anyone with a basic understanding of COBOL to interact and engage with your COBOL applications.

Everything you dreamed of in COBOL-based environments is now a reality. Visit CodeCatalyst.ai.

Todd Erickson is a tech writer with Phase Change Software. You can reach him at terickson@phasechange.ai.

June 26, 2017

Understanding code is the key to software development

June 26, 2017

By Elizabeth Richards and Todd Erickson

Discover how software developers are like archaeologists, and why understanding source code – the key to software development – involves a lot more than digging.

Software engineers are often called developers. However, according to a number of experienced programmers, they spend the great majority of their time (78%) simply searching and understanding existing software, and the rest of their time (22%) actually modifying legacy software or developing new applications.

Programmers are forced to spend so much time finding and comprehending code because current tools and techniques are not technically savvy, and they are too focused on the searching process. They don't help developers understand how the code works together within the systems they serve.

In fact, a recent blog post compared the source-code searching process to archeology. It's a reasonable analogy given that the tools developers use are only slightly more advanced than rudimentary shovels and brushes.

Why is software development – an activity that's driving incredible technological change – so far behind the curve in building tools that help programmers comprehend the code they work on?

Artifacts

Searching for ancient relics and specific lines of code are both complex processes.

An archeologist doesn't use a bulldozer and dig in random locations. She researches excavation sites and uses advanced technology, such as satellite imagery to find optimum exploration locales and ground-penetrating radar for mapping. Then she carefully and methodically removes topsoil while analyzing and recording each artifact down to the smallest pottery shard.

Every relic she unearths builds her knowledge of the site, and the people and culture she's investigating. For example, the archaeologist may assemble a handful of pottery shards into a serving dish, which she studies alongside other artifacts to better understand an ancient culture's family meal rituals. She can easily share the dish with other scholars and store it for future analysis.

Each discovered artifact may also modify how she approaches the rest of the dig.

Code

In software development, Professor Vaclav Rajlich asserts that the processes of searching and understanding code require two phases called concept location and impact analysis. Concept location involves finding the lines of code to be modified and the relevant but disjoined source surrounding it.

Impact analysis examines how a proposed modification will impact the entire application, including performance, stability, intent, and secondary consequences in distant modules. Poor or incomplete impact analysis can lead to more bugs.

In essence, a developer spends his time in active knowledge construction, building an integrated mental model so he understands how a system is constructed and its purpose and intended results. Only then can he be confident enough to make changes.

Errors logs, debuggers, and grepping assist developers in finding specific lines of code – his shards of pottery. But the developer must reconstruct the code into the mental models necessary for understanding the system. And these models remain locked away in the developer's mind, making them difficult to share and retrieve over time.

The greatest shovel ever invented

But it doesn’t have to be that way. Phase Change’s technology will transform how developers search and understand source code and applications. A programmer will no longer have to manually and laboriously search and play connect-the-dots to build mental models.

We are developing assistive artificial intelligence (AI) that automatically analyzes source code and understands the human intent behind it, creating immersive application-visualizations that resemble a programmer’s mental models. These visualizations can be stored, retrieved, and shared similar to how an archaeologist saves and shares the artifacts she assembles.

A developer will collaborate with our AI agent using natural language to effortlessly locate source and move quickly beyond simply identifying concept locations to performing comprehensive impact analysis. He will be aware of every effect his modifications bring about, and work confidently with a rich understanding of the code and the application.

Because the goal isn't to search, it's to understand.

learn more about our technology

 

 

Elizabeth Richards is Phase Change's director of business operations. You can reach her at erichards@phasechange.ai.
Todd Erickson is a tech writer with Phase Change. You can reach him at terickson@phasechange.ai.

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