Technical debt (also known as code debt, but can be also related to other technical endeavors) is a concept in software development that reflects the implied cost of additional rework caused by choosing an easy (limited) and fast solution now instead of using a better approach that would take longer. In short, when developers take shortcuts that enable them to quickly deliver features or functionality to keep up with deadlines and customer demand but with the risk of compromising software quality and maintainability.
Those kinds of easy, fast fixes may keep costs on the low in the short term, or keep software development and implementation projects on schedule, but they can also cause serious problems down the road if not addressed properly.
Before you can pay back technical debt, you must first be in a position to measure it. You can’t fix what you can’t measure. Although several tools are available to help assess technical debt, each one of them essentially checks software against a particular ruleset.
Six computer scientists at three universities across Greece* decided to evaluate the degree of the agreement among technical debt measurement tools. For that, they analysed 50 open source programs, with three commercial technologies. The results of this analysis, indicated that there was statistically strong agreement among the three technologies on the ordering of the classes based on their amount of technical debt. This was actually encouraging since each one measures technical debt in a different way and assesses different collections of weaknesses in developing their measure.
So, while there is no common method for measuring technical debt, the technology is getting closer and IT can use technical debt measures to improve applications.
*1 T. Amanatidis, N. Mittas, A. Moschou, A. Chatzigeorgiou, A. Ampatzoglou, & L. Angelis (2020). Evaluating the agreement among technical debt measurement tools: Building an empirical benchmark of technical debt liabilities. Empirical Software Engineering, 25 (5), 4161–4204.
Before you can pay back technical debt, you must first be in a position to measure it. You can’t fix what you can’t measure. Although several tools are available to help assess technical debt, each one of them essentially checks software against a particular ruleset.
Six computer scientists at three universities across Greece* decided to evaluate the degree of the agreement among technical debt measurement tools. For that, they analysed 50 open source programs, with three commercial technologies. The results of this analysis, indicated that there was statistically strong agreement among the three technologies on the ordering of the classes based on their amount of technical debt. This was actually encouraging since each one measures technical debt in a different way and assesses different collections of weaknesses in developing their measure.
So, while there is no common method for measuring technical debt, the technology is getting closer and IT can use technical debt measures to improve applications.
*1 T. Amanatidis, N. Mittas, A. Moschou, A. Chatzigeorgiou, A. Ampatzoglou, & L. Angelis (2020). Evaluating the agreement among technical debt measurement tools: Building an empirical benchmark of technical debt liabilities. Empirical Software Engineering, 25 (5), 4161–4204.