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"Requirements analysis"
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Disruptive analytics : charting your strategy for next-generation business analytics
Learn all you need to know about seven key innovations disrupting business analytics today. These innovations the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. -- Provided by publisher.
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User Requirements Analysis for Audiovisual Products Based on User Review Data
This study analyzed online review data to examine user requirements for audiovisual products and to compare requirement salience and satisfaction across traditional and emerging product contexts. We collected 86,213 Chinese-language reviews of Skyworth TVs, Xiaomi TVs, and Xiaomi projectors from JD.com. LDA topic modeling was used to identify major user requirement areas, and Logistic Regression, Random Forest, and Support Vector Machine (SVM) models were compared for sentiment classification, with the tuned SVM model retained for downstream analysis. The results show that user discussions primarily concern audiovisual experience, cost performance, service quality, design aesthetics, and intelligent operation. Skyworth TVs receive particularly strong evaluations for picture and sound quality (97.89% positive sentiment), whereas Xiaomi TVs are more strongly associated with cost-effectiveness and smart features (94.05% positive sentiment). Xiaomi projectors attract attention for portability but receive lower satisfaction ratings on core audiovisual performance and intelligent operation. These findings suggest that traditional manufacturers should continue strengthening core performance while improving service responsiveness, whereas emerging brands should build on their technological advantages while further enhancing their product reliability and user experience.
Journal Article
Customer Requirements Analysis and Product Service Improvement Framework Using Multi-Source User-Generated Content and Dual Importance–Performance Analysis: A Case Study of Fresh E-Ecommerce
The growth of e-commerce has led to a rapid increase in user-generated content (UGC), attracting scholars’ attention as a new data source for investigating customer requirements. However, existing requirements analysis methods fail to integrate three critical requirement indicators: stated importance, derived importance, and performance. Using only one or two of these indicators inevitably has its limitations. This paper proposes a novel framework for analyzing and prioritizing customer requirements based on multi-source UGC. First, customer requirements are extracted from online reviews and questions & answers using non-negative matrix factorization. Next, aspect-level sentiment analysis and multi-source data fusion are employed to calculate dual importance and performance. Specifically, we developed an improved importance–performance analysis (IPA) model, named dual importance–performance analysis (Du-IPA), which integrates the three indicators to classify requirement types in a 3D cube with corresponding improvement strategies. Finally, by combining the three indicators, an improved prospect value and PROMETHEE-II are proposed using prospect theory to prioritize CRs for product service improvement. The effectiveness of the proposed method is demonstrated through a case study of fresh food in online retail.
Journal Article
Re-Distill: A Multi-Stage Retrieval Framework for Functional–Non-Functional Requirement Linking in Software Engineering
Non-functional requirements (NFRs) are critical for ensuring software quality, yet they remain difficult to identify due to their implicit and loosely defined relationship with functional requirements (FRs). Existing research predominantly focuses on NFR classification, leaving the more practical problem of linking FRs with their corresponding NFRs largely underexplored. To bridge this gap, this research introduces Re-Distill, a framework that treats FR–NFR association as a retrieval task. It adopts a curriculum-guided, data-centric distillation strategy to improve semantic representations and capture the interdependencies between FRs and NFRs. The framework combines general semantic adaptation, domain-specific specialization, and teacher-guided hard-negative mining in a contrastive learning setting. During inference, it integrates dense and lexical retrieval with cross-encoder reranking to produce ranked NFR candidates for unseen FR queries. Experiments on an expanded FR–NFR dataset show consistent improvements throughout all training stages. The resulting model achieves a Recall@10 of 70.79%, significantly outperforming the zero-shot baseline (42.36% Recall@10). These results highlight the effectiveness of retrieval-based approaches for functional–non-functional requirement linking, providing a practical and scalable way to undertake software requirement analysis.
Journal Article
Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in requirements engineering (RE) due to the complexity of heterogeneous devices and dynamic user needs. Traditional RE methodologies often result in inefficient resource utilization and poor system performance. This research presents the goal-oriented requirements analysis (GORA) methodology, which optimizes requirement specification, resource allocation, and sustainability in IoT development. GORA addresses both functional and non-functional requirements, such as energy efficiency and security, while minimizing computational overhead and reducing resource wastage. The methodology integrates goal-oriented requirements analysis language i* and model-driven development (MDD) through a three-stage transformation process involving the i* RA model, UML class diagrams, and Python source code generation. A case study demonstrates how GORA improves system reliability, performance, and sustainability. Furthermore, an empirical evaluation was conducted in a simulated IoT environment, measuring key metrics such as energy consumption, latency, and code-generation error rate. A comparative analysis with existing RE and MDD approaches is also presented to highlight GORA’s advantages in feature coverage, sustainability support, and automation level. This work underscores the need for structured IoT RE frameworks and positions GORA as a foundation for future research in sustainable IoT development.
Journal Article
User Needs Analysis for the Definition of Operational Coastal Services
According to the global growth of the “Blue economy”, coastal zones are under pressure from both land and marine side economic activities. The fragmentation of sectorial interests and legislation along the coasts has led to the need for bridging knowledge (data/information and methods/tools) and governance (decision-makers at every level) in order to ensure sustainable economic development and social and ecosystem resilience. This poses the need for an interaction process that associates user needs to the European and national legislative framework to create a policy-oriented demand of Copernicus Earth Observation services in coastal areas. Such goals need a strong and effective system to monitor compliance and to assess the progress of the legislation. This study aims at identifying potential gaps in the current Copernicus product offer for the monitoring of the coastal sector through the elicitation of stakeholder requirements. The methodology is applied to the Italian landscape of users, but it is scalable at European level. The results provide a clear overview of the coastal user requirements, highlighting the common need of integrated information for the management, and represents the basis for defining the coastal services.
Journal Article
An ontology-based approach to engineering ethicality requirements
In a world where Artificial Intelligence (AI) is pervasive, humans may feel threatened or at risk by giving up control to machines. In this context, ethicality becomes a major concern to prevent AI systems from being biased, making mistakes, or going rogue. Requirements Engineering (RE) is the research area that can exert a great impact in the development of ethical systems by design. However, proposing concepts, tools and techniques that support the incorporation of ethicality into the software development processes as explicit requirements remains a great challenge in the RE field. In this paper, we rely on Ontology-based Requirements Engineering (ObRE) as a method to elicit and analyze ethicality requirements (‘Ethicality requirements’ is adopted as a name for the class of requirements studied in this paper by analogy to other quality requirements studied in software engineering, such as usability, reliability, and portability, etc. The use of this term (as opposed to ‘ethical requirements’) highlights that they represent requirements for ethical systems, analogous to how ‘trustworthiness requirements’ represent requirements for trustworthy systems. To put simply: the predicates ‘ethical’ or ‘trustworthy’ are not meant to be predicated over the requirements themselves). ObRE applies ontological analysis to ontologically unpack terms and notions that are referred to in requirements elicitation. Moreover, this method instantiates the adopted ontology and uses it to guide the requirements analysis activity. In a previous paper, we presented a solution concerning two ethical principles, namely Beneficence and Non-maleficence. The present paper extends the previous work by targeting two other important ethicality principles, those of Explicability and Autonomy. For each of these new principles, we do ontological unpacking of the relevant concepts, and we present requirements elicitation and analysis guidelines, as well as examples in the context of a driverless car case. Furthermore, we validate our approach by analysing the requirements elicitation made for the driverless car case in contrast with a similar case, and by assessing our method’s coverage w.r.t European Union guidelines for Trustworthy AI.
Journal Article
Requirements Analysis and Specification for a Molecular Tumor Board Platform Based on cBioPortal
Clinicians in molecular tumor boards (MTB) are confronted with a growing amount of genetic high-throughput sequencing data. Today, at German university hospitals, these data are usually handled in complex spreadsheets from which clinicians have to obtain the necessary information. The aim of this work was to gather a comprehensive list of requirements to be met by cBioPortal to support processes in MTBs according to clinical needs. Therefore, oncology experts at nine German university hospitals were surveyed in two rounds of interviews. To generate an interview guideline a scoping review was conducted. For visual support in the second round, screenshot mockups illustrating the requirements from the first round were created. Requirements that cBioPortal already meets were skipped during the second round. In the end, 24 requirements with sometimes several conceivable options were identified and 54 screenshot mockups were created. Some of the identified requirements have already been suggested to the community by other users or are currently being implemented in cBioPortal. This shows, that the results are in line with the needs expressed by various disciplines. According to our findings, cBioPortal has the potential to significantly improve the processes and analyses of an MTB after the implementation of the identified requirements.
Journal Article
Automating requirements analysis and test case generation
Writing clear and unambiguous requirements that are conflict-free and complete is no easy task. Incorrect requirements lead to errors being introduced early in the design process. The longer the gap between error introduction and error discovery, the higher the cost associated with the error. To address the growing cost of system development, we introduce a tool called Analysis of Semantic Specifications and Efficient generation of Requirements-based Tests (ASSERT™) for capturing requirements, backed by a formal requirements analysis engine. ASSERT also automatically generates a complete set of requirements-based test cases. The requirements are captured in a structured natural language that is both human- and machine-readable. Formal analysis of these requirements with an automated theorem prover identifies errors as soon as requirements are written. It also addresses the historical problem that analysis engines are hard to use and understand for someone without formal methods expertise and analysis results are often difficult for the end-user to understand and make actionable. ASSERT’s major contribution is to bring powerful requirements capture and analysis capability to the domain of the end-user. We provide explainable and automated formal analysis, something we found important for a tool’s adoptability in industry. Automating test case generation in ASSERT also provides clear and measurable productivity gains in system development.
Journal Article
Dataset Constrution through Ontology-Based Data Requirements Analysis
Machine learning (ML) technology is rapidly evolving, and the quality of ML systems is becoming an increasingly focal point of attention. Since the ML system is shaped by the dataset it learns from, its quality largely depends on the quality of the dataset. However, the dataset is often collected in a non-standardized process and few requirements and analysis methods are given to assist in identifying the needed dataset. This leads to no guarantee for the quality of dataset, affecting the generalization ability of model and resulting in low training efficiency. To address these issues, this paper proposes an ontology-based requirement analysis method where ontology integrates domain knowledge into the process of data requirements analysis and the coverage criteria on ontology are given for specifying data requirements which can later be used to guide the high-quality construction of the dataset. We held an experiment on an image recognition system in the field of autonomous driving to validate our approach. The result shows that the ML system trained by the dataset constructed through our data requirements analysis method has a better performance.
Journal Article