Automated nonfunctional requirements (NFRs) classification enhances consistency and traceability by systematically labeling requirements, saving effort, supporting early architectural and testing decisions, improving stakeholder communication, and enabling quality across diverse software domains. While prior work has applied natural language processing (NLP) and machine learning (ML) to NFR classification, existing datasets are often limited in size, domain diversity, and contextual richness. This study presents a novel dataset comprising over 2400 NFRs spanning 269 software projects across 26 software application domains, including nine blockchain projects. The raw requirements are standardized using Rupp’s boilerplate to reduce vagueness and ambiguity, and the classification of NFRs types follows ISO/IEC 25,010 definitions. We employ a range of traditional ML, deep learning (DL), and a transformer‐based model (i.e., BERT‐base) for automated classification of NFRs, evaluating performance across cross‐domain and blockchain‐specific NFRs. Results highlight that domain‐aware adaptation significantly enhances classification accuracy, with traditional ML and DL models showing strong performance on blockchain requirements. This work contributes a publicly available, context‐rich dataset and provides empirical insights into the effectiveness of NLP‐based NFR classification in both general and blockchain‐specific settings.

ABSTRACT Ambiguous user requirements present a challenge in software requirement engineering. A manual approach to handling ambiguity is time‐consuming. Software requirements are essential inputs to software development processes, including architecture and design, implementation, and testing. Requirement ambiguities lead to project cost overruns, delays in project delivery, and poor software product quality. Timely identification and correction of ambiguity can result in better software systems that meet product objectives and satisfy the needs of all stakeholders. This study explores various natural language processing techniques and SpanBERT (a variant of BERT). This research proposes a semiautomated approach for detecting anaphoric, coordination, and missing condition ambiguities in functional requirements. The proposed approach is validated on a new, original dataset containing 425 functional requirements from 16 domains. The ambiguities identified through our approach are compared with those detected manually and by ChatGPT. Our approach outperforms ChatGPT in detecting ambiguities. The proposed approach will aid project managers and requirement engineers in identifying ambiguities in requirement specifications, thereby helping to reduce cost overruns and delays in the software development process caused by requirement ambiguities.

Good planning and managing software test process require accurate estimation of software test effort. This becomes particularly significant when validation and verification activities are to be performed by an independent organization. This study presents a systematic literature review and a follow up industrial survey, which was performed to investigate the state of the art on software test effort estimation and the current practice of software industry in Turkey. The results showed that only few of the methods and metrics discussed in the literature are used by the industry. Furthermore, industrial participants have a general opinion that these methods could be improved by making use of additional metrics. Hence, there is a significant need for collaborative studies between industry and academia.