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Advanced AI technologies are serving humankind in a number of ways, fromhealthcare to manufacturing. Advanced automated machines are quite expensive,but the end output is supposed to be of the highest possible quality. Dependingon the agility of requirements, these automation technologies can changedramatically. The likelihood of making changes to automation software isextremely high, so it must be updated regularly. If maintainability is nottaken into account, it will have an impact on the entire system and increasemaintenance costs. Many companies use different programming paradigms indeveloping advanced automated machines based on client requirements. Therefore,it is essential to estimate the maintainability of heterogeneous software. As aresult of the lack of widespread consensus on software maintainabilityprediction (SPM) methodologies, individuals and businesses are left perplexedwhen it comes to determining the appropriate model for estimating themaintainability of software, which serves as the inspiration for this research.A structured methodology was designed, and the datasets were preprocessed andmaintainability index (MI) range was also found for all the datasets expect forUIMS and QUES, the metric CHANGE is used for UIMS and QUES. To remove theuncertainty among the aforementioned techniques, a popular multiple criteriadecision-making model, namely the technique for order preference by similarityto ideal solution (TOPSIS), is used in this work. TOPSIS revealed that GARFoutperforms the other considered techniques in predicting the maintainabilityof heterogeneous automated software.

A Multiple Criteria Decision Analysis based Approach to Remove Uncertainty in SMP Models