Introducing the Portfolio Advisor for Technical Debt
The latest release of CAST Highlight introduces a new capability called the Portfolio Advisor for Technical Debt. Users get a centralized, comprehensive view of tech debt on a single dashboard that includes new insights on obsolete technology versions (e.g., old versions of .NET, Java) alongside previously available tech debt insights on custom code and outdated open source components. Automated recommendations prioritize which applications represent the ‘bad debt’ to tackle first.
CAST Highlight Portfolio Advisor for Technical Debt
From this portfolio-level dashboard, users can map their application portfolios and create a shortlist of applications that represent their bad debt. In the real-world anonymized example depicted above, the total scope of tech debt is reduced from 200+ applications to a shortlist of 41 applications representing 20% of the total portfolio, a more manageable amount. Tech debt is further categorized by the specific type of debt including low software health in custom code, obsolete technology versions, and outdated open source components. Users can then drill down to individual applications to view tech debt insights within each application and get advice on how best to remediate it. This is one of nine new features introduced in the latest release of CAST Highlight. See the full release details here.
For more complex software systems, CAST offers CAST Imaging, which performs semantic analysis of applications to pinpoint the often small number of code flaws that create the majority of production issues.
The origin of tech debt
The term technical debt was originally coined by Ward Cunningham in the early 1990s. It is defined as shortcuts to speed up development (like skipping documentation, writing quick fixes, or not refactoring code) resulting in "debt" that’s just like financial debt. Tech debt accrues “interest” which can lead to increased maintenance costs, reduced agility, and more bugs over time if not "repaid" through refactoring and improvement.
A recent WSJ article shed more light on “The Invisible $1.52 Trillion Problem” that has come to dominate enterprise software modernization and move to cloud initiatives. Generative AI coding is compounding the problem with its introduction of debt-riddled code.
Approaching tech debt like financial debt
In the realm of finance, there is good debt, like a home mortgage with a low interest rate that builds credit, and bad debt, such as a high credit card balance with high interest rates that hurts credit scores. This is a good way to think about technical debt, too. There is good, or acceptable, debt represented by less business-critical applications with low tech debt density. And there is bad debt, represented by business-critical applications with high tech debt density. This can include low software health, outdated open source components, obsolete technologies, and other issues.
For organizations with hundreds or thousands of applications, the first challenge when tackling tech debt is narrowing the scope to be more manageable, and pinpointing where the bad debt is located across the portfolio. Then, it’s critical to map the different types of technical debt and how to tackle it. This is where software mapping & intelligence technology like CAST Highlight helps.
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