Technical Debt estimates and stats by technology – CAST

This documentation page is deprecated as the Technical Debt calculation methodology has been revisited. See this page instead. As detailed in the Indicator & Methodology deck, CAST Highlight leverages software quality measures and statistics provided by the application benchmarking solution CAST Appmarq to estimate the amount and density of technical debt that your application code base may have accumulated across iterations. The table below lists the technical debt statistics per line of code technology-wise.

Technology	Min.	Lower Quartile (25%)	Median (50%)	Upper Quartile (75%)	Max.
Abap/SAP	0.031606	1.09276	1.614014	1.76796	3.659672718
C	0.035744	1.202878	2.100075	2.937979	15.22273
COBOL	0.02	1.127387	1.881467	2.751253	8.799646
C++	0.55435	1.982766	3.544059	5.793916	20.76226334
C#	0.054696	2.539813	3.493363	4.89939	13.78143
Java	0.022764	3.712118	5.88523	7.856834	20.00208
JavaScript	0.022764	3.712118	5.88523	7.856834	20.00208
SHELL, BASH, KSH	0.031606	1.09276	1.614014	1.76796	3.659672718
Objective-C	0.55435	1.982766	3.544059	5.793916	20.76226334
PHP	0.022764	3.712118	5.88523	7.856834	20.00208
PL1	0.031606	1.09276	1.614014	1.76796	3.659672718
PL/SQL	1.892085	2.649112	2.863325	3.730918	4.430902
Transact-SQL	1.892085	2.649112	2.863325	3.730918	4.430902
VB/VB.Net	0.054696	2.539813	3.493363	4.89939	13.78143
JSP	0.022764	3.712118	5.88523	7.856834	20.00208
Python	0.031606	1.09276	1.614014	1.76796	3.659672718
Powerbuilder	0.054696	2.539813	3.493363	4.89939	13.78143

For reference only. For the complete details please refer the original article
https://doc.casthighlight.com/technical-debt-estimates-stats-technology/

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