Software Maintainability is one of the most important quality attributes. To increase quality of a software, to manage software more efficient and to decrease cost of the software, maintainability, maintainability estimation and maintainability evaluation models have been proposed. However, the practical use of these models in software engineering tools and practice remained little due to their limitations or threats to validity. In this paper, results of our Literature Review about maintainability models, maintainability metrics and maintainability estimation are presented. Aim of this paper is providing a baseline for further searches and serving the needs of developers and customers.
Recommender systems provide recommendations to users using background data such as ratings of users about items and features of items. These systems are used in several areas such as e-commerce, news websites and article websites. By using recommender systems, customers are provided with relevant data as soon as possible and are able to make good decisions. There are more studies about recommender systems and improving performance of them. In this study, prediction performances of neural networks were evaluated and their performances were improved using genetic algorithms. Performance of this study was compared with other studies, after that, superiority of this study was shown. While multilayer perceptron, generalized feed-forward network and CANFIS (Coactive Neuro Fuzzy Inference Systems) were used as a neural network algorithms, Movielens 100K and Movielens 1M datasets which are preferred in recommender systems studies mostly were used to train and test the system. Mean square error and root mean square error were employed as performance metrics. As a result, it was observed that genetic algorithm improves performance of neural networks and prediction performance of hybrid combination of neural networks and genetic algorithm is superior to prediction performance of recommender systems available in the literature.
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