Background: Data-driven requirement elicitation has been increasingly used in modern software engineering due to the growing availability of online user-generated textual data. However, existing approaches mostly rely on single-source data, which are limited in handling the diversity of characteristics found in online textual sources.
Objective: This study proposes an automated natural language processing (NLP)-based framework for requirement elicitation that integrates heterogeneous online sources, such as app reviews, online news, and tweets, for process innovation in the requirements engineering phase.
Methods: The proposed framework combines rule-based and AI-based extraction methods, semantic clustering, and diagram generation. Data were collected from application reviews, Twitter/X, and online news across six domains. The framework was evaluated using expert-annotated ground truth to measure extraction performance and expert-based assessments to examine clustering quality and artifact usefulness.
Result: AI-based extraction outperforms rule-based methods for requirements extraction, achieving F1 scores of 0.92, 0.80, and 0.67 on app reviews, 0.80 on Twitter, and 0.67 on online news. In the expert evaluation, the proposed system demonstrates high topic coherence, reduces elicitation time, and helps identify potential system requirements that may be overlooked in manual processes.
Conclusion: Multisource integration enhances the completeness and contextual richness of automated requirement elicitation. The proposed framework effectively transforms heterogeneous textual data into actionable requirement artifacts, providing a scalable and practical solution for early-stage software development.
Keywords: Requirement Elicitation, Natural Language Processing, Process Innovation, Multisource Data
A. Prasetya, Anindya Wita Wisesa, Eva Hariyanti et al.· Journal of Information Syste...· 0 citations
The rapid growth of global e-commerce has positioned information technology (IT) governance as a strategic necessity for organizations operating in the digital commerce landscape. This study conducts a systematic literature review (SLR) to identify, evaluate, and synthesize scientific literature on the application of IT governance in e-commerce. Following the Kitchenham & Charters guidelines and the PRISMA 2020 reporting protocol, 40 primary studies published between 2014 and 2025 were selected from four major academic databases: Scopus, SpringerLink, ScienceDirect, and IEEE Xplore. The review is guided by three research questions addressing the frameworks applied, their impacts on e-commerce performance, and existing research gaps. The findings reveal that COBIT is the most widely adopted framework (25%), followed by the TOE Framework (15%) and ITIL (12.5%). The reviewed studies generally report positive impacts across six categories: information security, operational efficiency, innovation capability, organizational agility, business performance, and customer trust and loyalty, while several studies identify strategic IT alignment as an important mediating factor. Several COBIT-based studies in Indonesian e-commerce contexts report IT governance maturity levels of 2–3, suggesting opportunities to strengthen proactive and strategic governance practices. Significant research gaps include limited geographic coverage, insufficient representation of SMEs, a lack of longitudinal studies, low framework integration, and minimal exploration of emerging e-commerce models, such as AI-driven commerce and social commerce. These findings provide a structured evidence base for practitioners seeking to strengthen IT governance maturity and researchers identifying future research directions.
Nania Nuzulita, Elga Ignafia, A. Azzahra et al.· bit-Tech· 0 citations