Technical Debt – Invisible Dinosaur
Technical Debt represents the effort required to fix issues that are embedded in the software implementation when an application is released to production. Technical Debt across different technology stacks, are estimated based on the number of engineering flaws and violations of good architectural and coding practices in the software implementation. This data-driven approach to analyze and articulate the hotspots and violations of engineering practices provides an objective and actionable estimate of Technical Debt.
Technical Debt calculation is the cost of fixing the structural quality issues in an application that, if left unfixed, put the business at critical risk. Technical Debt typically includes the issues that are highly likely to cause severe business disruption; and may not include all problems, just the most critically serious ones.
The estimation of the Technical Debt of an average-sized application of 300,000 lines of code (LOC) is around 3.6 $’s per LOC. Minimizing defects is one of the effective ways to keep development costs down and manage technical debts, which is a priority for many organizations. As the cost of fixing defects increases exponentially as software progresses through the development lifecycle, it’s critical to catch defects as early as possible. The costs of discovering defects after release are significant: up to 30 times more than if you catch them in the architectural and design phases.
Addressing Technical Debt
Poor software quality leads to huge technical debt and are common challenges in real-life software projects. Carrying out a software structural and functional assessment effectively and adopting the recommendations from it improves the design and implementation quality thus addressing the debts. A comprehensive assessment requires us to know the requirements in detail and weight different design aspects in accordance with the requirements. The amount of effort and time required to carry out a comprehensive design assessment can be quite high. The key is to proactively leveraging software quality assessment tools for structural and functional analysis thereby addressing technical debt.
These structural and functional quality assessment tools analyses code and identifies software quality issues. The tools analyse health, complexity & cost of application Portfolio, technical debts, architecture and system & code-level analytics. These tools provide insights into complex software structure to make informed decisions, communicate about software health, measure efficiency and prevent software catastrophes. The tools help you reduce technical debt and improve maintainability of the business-critical applications.
Studies have shown that a large percentage of software budgets are spent on identifying and correcting software defects. This makes sense to invest in technologies that can help cut these costs. Automated products can reduce the amount of time spent on software reviews, reducing development costs while improving time to market and efficiencies. These products can also easily identify issues that can be missed during a manual inspection, increasing overall code quality and therefore customer satisfaction. A well-rounded approach to software quality assessment includes automated products throughout the software development lifecycle—during the stages when code is being developed and after it is complete—to improve overall quality. Leveraging both static and dynamic analysis tools, organizations can improve quality throughout the software development lifecycle. Static analysis products examine the software without executing the program. They apply a set of rules to the software code that help identify structural and functional quality issues early in the development lifecycle. Static analysis can help you identify and eradicate flaws before your applications are deployed during the implementation phase, which usually results in a less costly remediation process. Dynamic analysis products monitor programs while they’re running, enabling you to identify run-time issues that can’t be detected by examining the developed code.
By combining the static and dynamic analysis products, you can improve your code quality, regardless of the individual skill level or whether the code was produced in-house or offsite. Together these analysers can automate and help development teams identify quality and compliance issues throughout the software development cycle. As a result, one can reduce the defects and technical debts in the applications, making them easier to maintain; decreasing development costs; and accelerating your time to market.
The views expressed in this article are the author’s views and AtoS does not subscribe to the substance, veracity or truthfulness of the said opinion.