Despite the fact that there are numerous classifications of technical debt based on various criteria, Code Debt or code smells is a category that appears in the majority of current research. One of the primary causes of code debt is the urgency to deliver software quickly, as well as bad coding practices. Among many approaches, static code analysis has received the most attention in studies to detect code-smell/code debt. However, most of them examine the same programming language, although today’s software company utilizes many development stacks with various languages and tools. This problem can be resolved by detecting code debt with Issue/Ticket cards. This paper presents a method for detecting code debt leveraging natural language processing on issue tickets. It also proposes a method for calculating the average amount of time that a code debt was present in the software. This method is implemented utilizing git mining.