  Advanced AI technologies are serving humankind in a number of ways, from
healthcare to manufacturing. Advanced automated machines are quite expensive,
but the end output is supposed to be of the highest possible quality. Depending
on the agility of requirements, these automation technologies can change
dramatically. The likelihood of making changes to automation software is
extremely high, so it must be updated regularly. If maintainability is not
taken into account, it will have an impact on the entire system and increase
maintenance costs. Many companies use different programming paradigms in
developing advanced automated machines based on client requirements. Therefore,
it is essential to estimate the maintainability of heterogeneous software. As a
result of the lack of widespread consensus on software maintainability
prediction (SPM) methodologies, individuals and businesses are left perplexed
when it comes to determining the appropriate model for estimating the
maintainability of software, which serves as the inspiration for this research.
A structured methodology was designed, and the datasets were preprocessed and
maintainability index (MI) range was also found for all the datasets expect for
UIMS and QUES, the metric CHANGE is used for UIMS and QUES. To remove the
uncertainty among the aforementioned techniques, a popular multiple criteria
decision-making model, namely the technique for order preference by similarity
to ideal solution (TOPSIS), is used in this work. TOPSIS revealed that GARF
outperforms the other considered techniques in predicting the maintainability
of heterogeneous automated software.
