Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub-projects with related SRs and devote each sub-project to a separate development team. However, functional SRs clustering has never been considered in the literature. Therefore, in this paper, we propose an approach to automatically cluster functional requirements based on semantic measure. An empirical evaluation is conducted using four open-access software projects to evaluate our proposal. The experimental results demonstrate that the proposed approach identifies semantic clusters according to well-known used measures in the subject.

In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in the recommendation systems. It is particularly useful for reducing data dimensions, capturing latent representations, and flexibly reconstructing various parts of input data. In this article, we propose an improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS) framework. This method aims to show better performance of the hybrid collaborative recommendation via semi-autoencoder (HRSA) technique. Two novel elements for iHSARS’s architecture have been introduced. The first element is an increase sources of side information of the input layer, while the second element is the number of hidden layers has been expanded. To verify the improvement of the model, MovieLens-100K and MovieLens-1M datasets have been applied to the model. The comparison between the proposed model and different state-of-the-art methods has been carried using mean absolute error (MAE) and root mean square error (RMSE) metrics. The experiments demonstrate that our framework improved the efficiency of the recommendation system better than others.