Technical Debt is the collection of design or implementation constructs that are expedient in the short term but set up a technical context that can make future changes more costly or impossible. In other words, Technical Debt is the deferment of good software design for the sake of expediency. It presents an actual or contingent liability that impacts internal system qualities, primarily maintainability and evolvability. It can slow the delivery of future releases or sprint increments, make defects harder to find and fix, and erode good testing practices.
A good software design does two things:
As the level of technical debt rises, so does the severity of the consequences like
We need to be able to enhance the functionality of our software without damaging any of the prior investments in functionality. Four core practices to reduce it
Challenge | Initial Step | Vision |
Process and Tools | Start aggressive initiatives to bring visibility to existing technical debt. Strategies include explicitly tagging technical debt issues, allocating resources for technical debt in the backlog and release planning, and reserving time to reflect on it during retrospectives. | Processes and tools manage it holistically throughout the lifecycle, enabling communication between stakeholders by evaluating intrinsic quality attributes. Few tools like Bliss, CAST Application Intelligence Platform, SonarQube, & Teamscale. |
Software economics | Track consequences of tradeoffs in terms of business and economic impact. | Technical debt management is used as a strategic software development approach. |
Software architecture | Use software architecture concretely during development, testing, and operations activities by managing quality attribute requirements and issues related to technical debt. | Focus on the source code and consequences on runtime behavior give way to managing technical debt at the level of architecture decisions and associated tradeoffs and risks. |
Empirical basis and data science | Instrument small changes in development activities to enable data collection, without overhead for teams, to provide input for establishing an empirical basis for technical debt management. Such information today includes iteration tempo, defect rate, bugs open for a long time, time spent on rework, files that are changed frequently, and so on. | Software development data is used for technical-debt analysis, with improved intelligent tools targeting developer productivity and efficiency. Validated models provide an empirical basis for decision-making. |
Education | Include it across the curriculum–such as how to avoid technical debt and how to use quality management tools and architecture reviews to uncover technical debt–spanning multiple courses or as a senior hands-on project. | It is an integral part of the curriculum, not as a separate course but as a learning thread permeating the course work. |
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