Whereas the activities of debt identification and measurement are broadly considered in literature, the activities of debt prioritization and communication lack appropriate approaches with an economic perspective. To address TD, various management activities are proposed, each addressing a particular purpose.
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Technical debt (TD) in a software system is a metaphor that tries to illustrate the remediation effort of the already introduced quality deficit and the impact thereof to the business value of the system. The accuracy that has been gained from using this approach reached 94.21%.
This approach utilizes the Genetic Algorithm (GA) to automate the composing of a detecting module that consists of a cohesion metric type and a coupling metric type and passes its resulting paired value to a deep learning approach to automate the detection of the large class bad smell. The questions remain: Can a module with the best combination of two metrics, for detecting the problem of large-class bad smell, be formed automatically rather than manually? And how is this double valued threshold determined to be used to infer the existence of this problem? This paper proposes the Hybrid Approach to detect Large Class Bad Smell (HA-LCBS). Selecting the right metrics to detect the large-class bad smell is a common problem, and it is usually accomplished manually. Examples of these metrics are size metrics, cohesion metrics, and coupling metrics. In Software Engineering (SE), metrics are used for detecting software design problems (bad smells) like the large-class bad smell, where a lot of different metrics were defined to find out the existence of this problem in the design of a class.
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