Validation of software requirements is a primary phase in requirements engineering that ensures requirements match the target system with the intended needs of the acquirer. It aims to detect and correct errors that prevail in the specified requirements. Although there are tremendous requirements validation approaches, some software may fail because of limited or ineffective requirements validation techniques and unreliable requirements’ quality characteristics. In this study, a systematic literature review of requirements validation is performed. The study analyzes the most adopted validation techniques, reports requirements quality characteristics, and discovers significant challenges of validation techniques. The review identified 66 relevant primary studies analyzed to derive deep insights into the following aspects of requirements validation: trends of requirements validation methods, including their subtechnique strengths and weaknesses, requirements quality characteristics categories, and adopted tools and datasets in these techniques. We grouped validation techniques into categories: prototyping, inspection, knowledge-oriented, test-oriented, modeling and assessment, and formal models. The analysis reported 19 validation techniques, 27 tools, new requirements validation characteristics, and several challenges that prevailed through validation techniques. The trend of validation techniques is to those methods that apply machine learning techniques with knowledge from dictionaries and ontologies. Most challenges are about how to express the requirements and how to revert clients’ feedback. There is a strong relationship between validation techniques, software application domain, and requirements validation quality attributes. Thus, there is an immense need to unify the quality characteristics and domain-specific validation methods.

Purpose: – The purpose of this paper is to propose a holistic cyber security implementation framework (HCS-IF) that lays out the ground for a conceptual, coherent, systematic, overarching and consolidated approach to implement cyber security strategies (CSSs). Design/methodology/approach: – The HCS-IF is conceptually proposed to address the actual needs that are extracted from literature review. The HCS-IF uses and integrates a set of high-level conceptual security controls, solutions, processes, entities, tools, techniques or mechanisms that are already known in the domains of information security management, software engineering and project management to address the identified needs. Findings: – The HCS-IF components and controls collectively interact and cooperate to implement CSSs. The proposed framework is compared with other related frameworks, and the results show that the HCS-IF outperforms other frameworks on most of the suggested comparison criteria. Originality/value: – From a practical standpoint, governments and practitioners alike stand to gain from the findings of this research. Governments who want to implement CSSs on a national level will find the proposed framework useful in overseeing cyber security implementation. Practitioners will be prepared to address the anticipated cyber security implementation challenges and the required controls needed to facilitate cyber-security implementation in a holistic overarching manner.

Text similarity plays an important role in natural language processing tasks such as answering questions and summarizing text. At present, state-of-the-art text similarity algorithms rely on inefficient word pairings and/or knowledge derived from large corpora such as Wikipedia. This article evaluates previous word similarity measures on benchmark datasets and then uses a hybrid word similarity in a novel text similarity measure (TSM). The proposed TSM is based on information content and WordNet semantic relations. TSM includes exact word match, the length of both sentences in a pair, and the maximum similarity between one word and the compared text. Compared with other well-known measures, results of TSM are surpassing or comparable with the best algorithms in the literature.

Traditional software effort estimation methods, such as term frequency–inverse document frequency (TF-IDF), are widely used due to their simplicity and interpretability. However, they struggle with limited datasets, fail to capture intricate semantics, and suffer from dimensionality, sparsity, and computational inefficiency. This study used pre-trained word embeddings, including FastText and GPT-2, to improve estimation accuracy in such cases. Seven pre-trained models were evaluated for their ability to effectively represent textual data, addressing the fundamental limitations of TF-IDF through contextualized embeddings. The results show that combining FastText embeddings with support vector machines (SVMs) consistently outperforms traditional approaches, reducing the mean absolute error (MAE) by 5–18% while achieving accuracy comparable to deep learning models like GPT-2. This approach demonstrated the adaptability of pre-trained embeddings for small datasets, balancing semantic richness with computational efficiency. The proposed method optimized project planning and resource allocation while enhancing software development through accurate story point prediction while safeguarding privacy and security through data anonymization. Future research will explore task-specific embeddings tailored to software engineering domains and investigate how dataset characteristics, such as cultural variations, influence model performance, ensuring the development of adaptable, robust, and secure machine learning models for diverse contexts.