The Data Science and Empirical Basis of Tech Debt

To manage tech debt effectively, you should implement and instrument some small changes in the developmental activities. This will enable you in the collection of data without any overhead on the development teams. You will be able to provide any input to establish an empirical basis for tech debt management. Such information includes defect rates, iteration tempo, time spent on rework, bugs open for a long time, files that you have to change frequently and much more. There are software development data which is used for analysis, identification, and quantification of tech debt with improved and intelligent tools. These are meant to target the efficiency and productivity of the software development team.

Empirical Basis for Decision Making

Several models are validated to provide the empirical basis which will help in the decision making. Educating your teams, both technical as well as non-technical is the best way in dealing with tech debt. You should include a training program on a regular basis in the curriculum. Such training sessions should include the ways in which tech debt can be avoided as well as ways in which quality tech debt management tools can be used. Also, include in it the architecture reviews to make tech debt visible to all. When you make tech debt an integral part of the curriculum and not as a separate course, such learning thread will permeate through the course work as well.

Convergence of Efforts 

Such convergence of efforts will make software development economically and technically sustainable. This will help in reducing the friction that slows the software evolution and its machinery which may threaten the ability of the discipline to maintain the code base on which the community depends. The journey from metaphor to practice can be easily completed with surveying the landscape of software engineering to scope the concept and to understand tech debt. Such landscape should include intentional tech debt that is often related to the architecture of the system, tech debt due to changes made in the context leading to technological gaps and also tech debts that have smaller granularity resulting in a low internal quality of codes mostly.

Boundaries of the Landscape  

You have to establish the boundary for the landscape on maintenance and evolution. Things like new features that are not yet implemented lack of process and defects should lie outside the boundary and not be considered as tech debt. Distinguish the internal qualities in a system that is visible to the user from those which are no visible. Most defects in a system have a current and immediate impact, both positive and negative.

Defects and Tech Debt

Defects are often considered as the symptoms of tech debt which you can see or feel only in the future when you will have to incur additional cost. Therefore, you and your team along with the shareholders should understand the advanced concept of tech debt as your overall investment strategy. The benefit of such advance science of tech debt is in the greater returns you will enjoy a debt free development process.

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