CISQ has developed an automated specification to measure Technical Debt. Technical Debt is a measure of software cost, effort, and risk due to defects remaining in code at release. Like financial debt, Technical Debt incurs interest over time in the form of extra effort and cost to maintain the software. Technical Debt also represents the level of risk exposed to business due to the increased cost of ownership.
The Technical Debt specification utilizes CISQ’s Automated Quality Characteristic Measures to measure the impact of critical violations of good coding and architectural practice in the source code of software.
The Technical Debt specification was formally approved as a standard at the OMG® Technical Meeting, September 25-29, 2017 in New Orleans, LA, USA.
Track finalization of the OMG® standard here: http://www.omg.org/spec/ATDM/
Most Technical Debt is assessed using a linear model – that is, by looking at all code quality issues in the same way. CISQ has seen through our research that some coding issues have much more impact than others – typically the architectural defects, rather than the unit-level defects. CISQ’s approach to Technical Debt measurement takes into account the relative impact of coding defects.
You are invited to a special CISQ webinar to learn more:
Title: New Automated Technical Debt Standard
Date: November 2, 2017 from 11:00am – 11:30am ET
Speaker: Dr. Bill Curtis, Executive Director, Consortium for IT Software Quality
The CISQ measure of Automated Technical Debt has just been approved by the OMG® as a standard for measuring the future cost of defects remaining in system source code at release. The ripple effects from Technical Debt can hinder innovation and put businesses at unacceptable levels of risk, including high IT maintenance costs, outages, breaches, and lost business opportunities.
In this webinar, Dr. Bill Curtis, Executive Director of CISQ, will introduce the new Technical Debt measure and outline how the specification is composed. He will present a full picture of the Technical Debt metaphor and how it can be used to communicate IT issues to the business. The measure is ready to be used by vendors of static code analysis (SCA) tools that detect violations of good coding and architectural practice in software. He will present a process for steadily reducing Technical Debt in critical business applications. Business leaders will learn how to use the measure to manage and reduce IT risk.