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- W4312219886 abstract "Software has to be updated frequently to match the customer needs. If software maintainability is not given priority, it affects the software development life cycle and maintenance expenses, which deplete organizational assets. Before releasing software, maintainability must be estimated, as the impact of bugs and errors can affect the cost and reputation of the organization after deployment. Regardless of the programming paradigm, it's important to assess software maintainability. Many software maintainability prediction models' compatibilities with new programming paradigms are criticized because their limited applicability over heterogeneous datasets. Due this challenge small and medium-sized organizations may even skip the maintainability assessment, resulting in huge lose to such organizations. Motivated by this fact, we used Genetic Algorithm optimized Random Forest technique (GA) for software maintainability prediction models over heterogeneous datasets. To find optimal model for software maintainability prediction, the Technique for Order preference by Similarity to Ideal Solution (TOPSIS), a popular multiple-criteria decision-making model, is adopted. From the results, it is concluded that the GA is optimal for predicting maintainability of software developed in various paradigms." @default.
- W4312219886 created "2023-01-04" @default.
- W4312219886 creator A5013054905 @default.
- W4312219886 creator A5065197324 @default.
- W4312219886 date "2022-12-26" @default.
- W4312219886 modified "2023-09-26" @default.
- W4312219886 title "A multiple criteria decision analysis based approach to remove uncertainty in SMP models" @default.
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- W4312219886 doi "https://doi.org/10.1038/s41598-022-27059-0" @default.
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