As one of the most important concepts in the software engineering process, requirements engineering plays an important task in sustainability engineering by understanding the nature of software system and their impacts on the entire dimension of sustainable development. Unfortunately, the process for incorporating sustainability concerns is not a trivial task. Sustainability is a concept with a high level of abstraction and is mostly treated as an afterthought. This work discusses a practical approach on how to capture requirements with sustainability concern related to software development. A stepwise guideline is presented to ease the requirements engineering process that can address sustainability issues. The proposed guideline combined well-known approaches such as the goal-scenario-based approach, analytical hierarchical approach, and feature modeling to capture sustainability requirements as an integrated framework. altogether, this tutorial shows a practical methodology in which the engineer can see which aspects or features that they need to improve to meet the required sustainability indicator and baseline.

Software sustainability evaluation has become an essential component of software engineering (SE) owing to sustainability considerations that must be incorporated into software development. Several studies have been performed to address the issues associated with sustainability concerns in the SE process. However, current practices extensively rely on participant experiences to evaluate sustainability achievement. Moreover, there exist limited quantifiable methods for supporting software sustainability evaluation. Our primary objective is to present a methodology that can assist software engineers in evaluating a software system based on well-defined sustainability metrics and measurements. We propose a novel approach that combines machine learning (ML) and software analysis methods. To simplify the application of the proposed approach, we present a semi-automated tool that supports engineers in assessing the sustainability achievement of a software system. The results of our study demonstrate that the proposed approach determines sustainability criteria and defines sustainability achievement in terms of a traceable matrix. Our theoretical evaluation and empirical study demonstrate that the proposed support tool can help engineers identify sustainability limitations in a particular feature of a software system. Our semi-automated tool can identify features that must be revised to enhance sustainability achievement.

Abstract Due to the critical role of a software‐intensive system in society, software engineers have a responsibility to add sustainability as a goal while developing a software system. However, there is a lack of practical guidelines providing a tangible decomposition of the sustainability aspect. Moreover, there are limited quantifiable methods to support sustainable design and analysis. Therefore, we propose a systematic approach that allows software practitioners to accommodate sustainability concerns that are aligned with other software quality attributes to enhance sustainable development. By using the combination of a goal‐scenario‐based approach and feature modeling, sustainability requirements are elicited along with their functional compositions. Our approach is a comprehensive reference model that decomposes sustainability requirements, enabling analysis, support, and assessment of sustainability risk analysis and guiding the designer to construct a feature model as a system functional decomposition. From the conducted theoretical evaluation and empirical study, our proposed approach can derive more significant sustainability‐related requirements and key features by providing a practical guideline with the integration of well‐known methods to address sustainability in requirements engineering. With the help of the provided approach, we can solve more conflicting goals in different sustainability dimensions.

National happiness has been actively studied during last ten years.The factor of happiness could be different due to different human perspective.The factors used in this work include both physical needs and the mental needs of humanity such as educational factor.This work identified more than 90 features that can be used to predict the country happiness.Unfortunately, manually analyzing the features is difficult and needs a lot of resources.Due to numerous size of the features, it is unwise to rely on the prediction of national happiness by manual analysis.That process will result in the high cost of analysis.Therefore, this work used machine learning technique which is a Support Vector Machine to learn and predicts the country happiness.Dimensionality reduction is also done in this work. Using the information gain technique, the features can be reduced.This technique is chosen due to its ability to explore the interrelationships among a set of variables.The selected features are also evaluated using the SVM classifier.Using the data of 187 countries from the UN Development Project, this work is able to identify which factor needed to be improved by a certain country to increase the happiness of their citizens.

National happiness has been actively studied throughout the past years. The happiness factor varies due to different human perspectives. The factors used in this work include both physical needs and the mental needs of humanity, for example, the educational factor. This work identified more than 90 features that can be used to predict the country happiness. Due to numerous features, it is unwise to rely on the prediction of national happiness by manual analysis. Therefore, this work used a machine learning technique called Support Vector Machine (SVM) to learn and predict the country happiness. In order to improve the prediction accuracy, dimensionality reduction technique which is the information gain was also used in this work. This technique was chosen due to its ability to explore the interrelationships among a set of variables. Using data of 187 countries from the UN Development Project, this work is able to identify which factor needed to be improved by a certain country to increase the happiness of their citizens.