Explore the vast range of titles available.

The modern software ecosystems employ application programming interface (API) to allow interoperability and integration across various platforms. Non-functional requirements (NFRs) as quality attributes are as vital as functional requirements (FRs) in assuring software quality and user satisfaction. API quality management is a challenge to software industry owing to poor NFRs handling during API development, resulting in cost increase, project failure and dissatisfied customers. The existing standards failed in producing a comprehensive quality framework for APIs. OpenAPI Specification (OAS) focuses on functional aspects, whereas ISO/IEC 25010:2023 emphases on traditional software quality. Hence, this study proposes a novel non-functional requirement quality framework for APIs (NFRQF-API). In compliance with ISO/IEC 25,010, the framework includes key NFRs and their influencing factors to facilitate the development of high-quality APIs. Explanatory sequential mixed-methods design was applied in this study, involving a survey tool. Data were collected using a questionnaire comprising eight Likert-scale items and two open-ended items. Descriptive statistics and reliability testing were applied for quantitative data analysis, while thematic analysis was applied to evaluate the qualitative data, which supports in achieving a comprehensive expert-based justification for the conceptual feasibility and applicability of framework. Based on the experts’ survey, analyses results demonstrated the potential effectiveness of the proposed framework in managing the Core, Critical, and Contextual NFRs in the entire API development lifecycle. This study bridges the gap between existing quality models and API quality assurance challenges. The structured expert-refined framework facilitates the enhancement of API quality management and offers KPI-based conceptual quality standards for the industry to design high-quality APIs by effectively handling critical NFR issues.

Recently, deep learning (DL) has been utilized successfully in different fields, achieving remarkable results. Thus, there is a noticeable focus on DL approaches to automate software engineering (SE) tasks such as maintenance, requirement extraction, and classification. An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models—long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN)—in order to classify the software requirement (SR) specification, the binary classification of SRs into functional requirement (FRs) or non-functional requirements (NFRs), and the multi-label classification of both FRs and NFRs into further experimental classes. The models were trained and tested on the PROMISE dataset. A one-phase classification system was developed to classify SRs directly into one of the 17 multi-classes of FRs and NFRs. In addition, a two-phase classification system was developed to classify SRs first into FRs or NFRs and to pass the output to the second phase of multi-class classification to 17 classes. The experimental results demonstrated that the proposed classification systems can lead to a competitive classification performance compared to the state-of-the-art methods. The two-phase classification system proved its robustness against the one-phase classification system, as it obtained a 95.7% accuracy in the binary classification phase and a 93.4% accuracy in the second phase of NFR and FR multi-class classification.

Software systems are becoming increasingly complex. Their ubiquitous presence makes users more dependent on their correctness in many aspects of daily life. As a result, there is a growing need to make software systems and their decisions more comprehensible, with more transparency in software-based decision making. Transparency is therefore becoming increasingly important as a non-functional requirement. However, the abstract quality aspect of transparency needs to be better understood and related to mechanisms that can foster it. The integration of explanations into software has often been discussed as a solution to mitigate system opacity. Yet, an important first step is to understand user requirements in terms of explainable software behavior: Are users really interested in software transparency and are explanations considered an appropriate way to achieve it? We conducted a survey with 107 end users to assess their opinion on the current level of transparency in software systems and what they consider to be the main advantages and disadvantages of embedded explanations. We assess the relationship between explanations and transparency and analyze its potential impact on software quality. As explainability has become an important issue, researchers and professionals have been discussing how to deal with it in practice. While there are differences of opinion on the need for built-in explanations, understanding this concept and its impact on software is a key step for requirements engineering. Based on our research results and on the study of existing literature, we offer recommendations for the elicitation and analysis of explainability and discuss strategies for the practice.

The correct classification of requirements has become an essential task within software engineering. This study shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions “Which works best (Bag of Words (BoW) vs. Term Frequency–Inverse Document Frequency (TF-IDF) vs. Chi Squared (CHI2)) for classifying Software Requirements into Functional Requirements (FR) and Non-Functional Requirements (NF), and the sub-classes of Non-Functional Requirements?” and “Which Machine Learning Algorithm provides the best performance for the requirements classification task?”. The data used to perform the research was the PROMISE_exp, a recently made dataset that expands the already known PROMISE repository, a repository that contains labeled software requirements. All the documents from the database were cleaned with a set of normalization steps and the two feature extractions, and feature selection techniques used were BoW, TF-IDF and CHI2 respectively. The algorithms used for classification were Logist Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB) and k-Nearest Neighbors (kNN). The novelty of our work is the data used to perform the experiment, the details of the steps used to reproduce the classification, and the comparison between BoW, TF-IDF and CHI2 for this repository not having been covered by other studies. This work will serve as a reference for the software engineering community and will help other researchers to understand the requirement classification process. We noticed that the use of TF-IDF followed by the use of LR had a better classification result to differentiate requirements, with an F-measure of 0.91 in binary classification (tying with SVM in that case), 0.74 in NF classification and 0.78 in general classification. As future work we intend to compare more algorithms and new forms to improve the precision of our models.

The web service technology has still proved its effectiveness in the digital revolution we are facing. This success unfortunately raises more and more complex obstacles, particularly related to the service composition. The integration of Non-Functional Requirements (NFRs) in each step of service composition process, starting with abstract service composition specification to the generation of the verified and concrete composed services, represents one of them. Furthermore, this complexity remains more difficult when NFRs are addressed in both quantifiable (i.e. Quality of Service) and behavioral aspects. Despite the relevant contributions present in the literature, this challenge still remains an open issue when considering NFRs modeling, publishing, integrating with each other, and handling conflicts and dependencies in the whole composition’s lifecycle. As a consequence, we suggest this contribution that aims to propose an approach showing how to weave efficiently required NFRs with functional requirements in a complete lifecycle composition supporting specification, formalization, model checking verification and integration steps of desired concrete composite service. Patient Health Records in Regional and University Health Centers in Morocco is used as a case study to experiment our approach.

Systems that rely on Machine Learning (ML systems) have differing demands on quality—known as non-functional requirements (NFRs)—from traditional systems. NFRs for ML systems may differ in their definition, measurement, scope, and comparative importance. Despite the importance of NFRs in ensuring the quality ML systems, our understanding of all of these aspects is lacking compared to our understanding of NFRs in traditional domains. We have conducted interviews and a survey to understand how NFRs for ML systems are perceived among practitioners from both industry and academia. We have identified the degree of importance that practitioners place on different NFRs, including cases where practitioners are in agreement or have differences of opinion. We explore how NFRs are defined and measured over different aspects of a ML system (i.e., model, data, or whole system). We also identify challenges associated with NFR definition and measurement. Finally, we explore differences in perspective between practitioners in industry, academia, or a blended context. This knowledge illustrates how NFRs for ML systems are treated in current practice, and helps to guide future RE for ML efforts.

In the era of environmental degradation and resource scarcity, the concept of circular economy (CE) has emerged as a pivotal strategy to transform the contemporary industrial landscape. As an integral component of the 10R framework, remanufacturing is emerging as a production strategy that revitalizes end-of-life (EOL) products to a like-new condition, fostering a more sustainable production and consumption. Despite its immense environmental and economic benefits, the implementation of remanufacturing practices is confronted with a multitude of challenges, including sourcing of EOL products, managing component variability, and arbitrary failure rates that result in major process inefficiencies. This paper embarks on the definition of functional and non-functional requirements for remanufacturing production planning and control (RPPC) to establish a systematic approach to address the existing challenges and uncertainties that arise in remanufacturing systems. Based on the synthesis of a comprehensive literature study, eight functional requirements and a total of 48 associated key performance measures are derived and contextualized in a coherent conceptual framework. This establishes a consensus to mitigate the impacts caused by uncertainty in remanufacturing. The feasibility of the conceptual framework is validated in an industrial case study with an OEM remanufacturer of electric power steering products. The findings of this research paper advance the field of RPPC and offer guidance to industrial decision-makers to evaluate and optimize their remanufacturing production systems.

Mining activity has been and is one of the most important and indispensable industries for the development of society. Given its role in the provision of raw materials, advancing the development of environmentally friendly mining practices is essential for meeting the globally established goals of sustainable development. In this regard, actions and incentives are being promoted by the European Union, such as the Mine.io project presented in this research. In response to the needs identified within the mining sector, this research seeks to explore the functional and non-functional requirements across several mining contexts. The objective is to establish effective patterns that positively influence the sector activities. This effort is envisioned as a critical foundation for developing a digital architecture that addresses sector limitations and fosters the integration of Industry 4.0 principles into the mining domain. The results provide a solid basis for understanding the needs of the different mining sectors analyzed, while also demonstrating the potential advancements achievable through the project’s technological developments. They enable a comprehensive evaluation of the current technological state in relation to the broader context of global legacy practices, establishing informed guidelines for effective sector responses based on digitalization and the application of sustainable tools.

User reviews obtained from mobile application (app) stores contain technical feedback that can be useful for app developers. Recent research has been focused on mining and categorizing such feedback into actionable software maintenance requests, such as bug reports and functional feature requests. However, little attention has been paid to extracting and synthesizing the Non-Functional Requirements (NFRs) expressed in these reviews. NFRs describe a set of high-level quality constraints that a software system should exhibit (e.g., security, performance, usability, and dependability). Meeting these requirements is a key factor for achieving user satisfaction, and ultimately, surviving in the app market. To bridge this gap, in this paper, we present a two-phase study aimed at mining NFRs from user reviews available on mobile app stores. In the first phase, we conduct a qualitative analysis using a dataset of 6,000 user reviews, sampled from a broad range of iOS app categories. Our results show that 40% of the reviews in our dataset signify at least one type of NFRs. The results also show that users in different app categories tend to raise different types of NFRs. In the second phase, we devise an optimized dictionary-based multi-label classification approach to automatically capture NFRs in user reviews. Evaluating the proposed approach over a dataset of 1,100 reviews, sampled from a set of iOS and Android apps, shows that it achieves an average precision of 70% (range [66% - 80%]) and average recall of 86% (range [69% - 98%]).