OMG Technical Debt - an explanation

Introduction

Technical Debt (OMG) is calculated as the amount of effort required to fix the structural quality problems in an application that, if left unfixed, put the business at serious risk. Like financial debt, Technical Debt incurs interest in the form of the extra effort it takes to maintain and enhance an application due to the structural quality flaws in the code.

Technical Debt (OMG) follows the OMG specification. For more information on the specification, please visit - https://www.omg.org/spec/ATDM/About-ATDM/.

Background of Technical Debt

The Technical Debt metaphor was introduced by Ward Cunningham to describe how sub-optimal design decisions, often made to meet schedules, accumulated a debt that had to be repaid through corrective maintenance during future releases.

The violations of choosing “debt” as a metaphor engages a set of financial concepts that help executives think about software quality in business terms. The components that comprise Technical Debt provide a foundation for the economics of software quality. The metaphor can be partitioned into the following elements:

  • Technical Debt - Future costs attributable to known structural weaknesses in production code that must be fixed. Technical Debt includes both the debt’s principal and interest. A weakness in production code is only included in Technical Debt calculations if those responsible for the application believe it is a ‘must-fix’ problem, therefore incurring corrective maintenance costs in a future release. Technical Debt is a primary component of the cost of application ownership.
  • Principal - The cost of remediating must-fix problems in production code. At a minimum, the principal is calculated from the number of hours required to remediate these problems, multiplied by the fully burdened hourly cost of those involved in designing, implementing, and unit testing these fixes.
  • Interest - Continuing costs, primarily in IT, attributable to must-fix problems so long as they remain in production code. These ongoing costs can result from the excessive effort to modify unnecessarily complex code, greater resource usage by inefficient code, etc.
  • Business Risk – Potential costs to the business if must-fix problems in production code cause damaging operational events such as outages, data corruption, performance degradation, and security breaches.
  • Liability - Costs to the business resulting from operational problems caused by flaws in production code. These flaws include both must-fix problems included in the calculation of Technical Debt as well as problems not listed as must-fix because their risk was underestimated.
  • Opportunity Cost - Benefits such as revenue from new features that could have been achieved had resources been committed to developing new capability rather than being assigned to retire Technical Debt. Opportunity costs represent the trade-off that application managers and executives must weigh when deciding how much effort to devote to retiring Technical Debt.

CISQ Quality Characteristic Measures

CISQ has developed a specification for Technical Debt that is designed to predict corrective maintenance costs and related factors to guide IT decisions and resource allocations. The CISQ measure of Technical Debt builds on the existing four OMG standards CISQ has developed for measuring the structural quality of software.

The Automated Technical Debt Measure (ATDM) is calculated from occurrences of the 86 weaknesses that are included in the 4 CISQ Quality Characteristic measures. Detecting and counting these weaknesses is the starting point for calculating ATDM. The CISQ Quality Characteristic measures consist of the following approved specifications of the OMG:

  • Automated Source Code Reliability Measure (ASCRM) - violations of good architectural and coding practice that can cause outages, delayed recovery, data corruption, and unpredictable operational behavior.
  • Automated Source Code Security Measure (ASCSM) - violations of good architectural and coding practice in an application that allow unauthorized intrusion into the application’s source code, data store, operations, or connections.
  • Automated Source Code Performance Efficiency Measure (ASCPEM) - violations of good architectural and coding practice that can result in slow response, degraded performance, or excessive use of computational resources.
  • Automated Source Code Maintainability Measure (ASCMM) - violations of good architectural and coding practice that make an application’s source code difficult to understand or modify

For more information about CISQ patterns, please visit - https://www.it-cisq.org/standards/code-quality-standards/

How OMG specified Technical Debt measure is calculated

The calculation of OMG specified Technical Debt requires the installation of an extension OMG Automated or Contextual Technical Debt Measure and the generation of a new snapshot with this extension installed.

Release ≥ 2.x of this extension uses CTDM (Contextual Technical Debt Measure) to calculate Technical Debt values. CTDM is a recognized nomenclature for a customized ATDM (Automated Technical Debt Measure) as described in chapter 6.3 of the OMG ATDM specification 1.0, see: https://www.omg.org/spec/ATDM/1.0/PDF. For this extension, CTDM uses a combination of remediation effort produced by the ISO-5055 measure and remediation effort produced by CAST Imaging Core.

Technical Debt measure is calculated as the total effort required to remediate all CISQ patterns. A Pattern (or Source Code Pattern) designates a set of elements and their relationships that can be detected through automated matching of the pattern description with structures in the source code.

A pattern remediated effort is derived by summing up all the occurrence remediation effort. An occurrence (or Pattern Occurrence) designates a single instance of a Source Code Pattern (or Pattern) representing a weakness that has been implemented in the measured software (ASCMM, ASCRM, ASCPEM, ASCSM):

 

The occurrence remediation effort is the multiplication of occurrence raw remediation effort and adjustment factor. The occurrence raw remediation effort has been estimated based on survey results and is fixed input to the calculation engine. The adjustment factor contextualizes the occurrence remediation effort based on various criteria as described below.

  • Complexity - or Effort Complexity – of the code elements implementing an Occurrence is qualification information that is measured according to the Effort Complexity definition from the Automated Enhancement Points (AEP) specification. (AEP)
  • Exposure - of code elements propagating effects of the occurrence to the rest of the software. Based on the extent of propagation, remediating the occurrence could involve direct references to code elements (measured as the code elements’ number of distinct direct callers), or indirect references (measured as the number of distinct call paths leading to the code elements).
  • Technological diversity - the number of the languages in which elements in the source code pattern of a specific occurrence are instantiated.
  • Concentration - Concentration is qualification information that measures the number of Occurrences within any Code Element in the software. Total number of occurrences of any source code patterns within a single code element (e.g., class, module, component, subroutine, etc.).
  • Evolution status - The Evolution Status of an Occurrence and of code elements implementing an Occurrence is qualification information which indicates if the Occurrence or the code elements implementing an Occurrence have been added, updated, or deleted between measured revisions of the software. Changes and evolution both of code elements in the occurrence and of code elements constituting the immediate software environment within which the occurrence is embedded.
  • Occurrence Gap Size - In the context of patterns which rely on roles that model values and threshold values that are not to be exceeded, the gap between these values must be closed to remediate this weakness; the Occurrence Gap Size is the extent of the gap, measured as the difference between the values and the thresholds.