In agile development, user stories are used to express user requirements, which employ simple natural language sentences. As software scales grow and evolve continuously, managing user stories becomes increasingly challenging, necessitating the modeling of user stories to clarify their associated semantics. Currently, representing user stories as goal models is an effective approach. However, extracting the semantics of the goal models from user stories is a time-consuming and labor-intensive task. In particular, abstracting low-level user stories into high-level goals remains difficult and relies heavily on the expertise of requirements engineers. This paper leverages the knowledge extraction and comprehension capabilities of large language models (LLMs) to divide the goal model extraction task into two categories: explicit concept extraction and implicit concept extraction. This classification is based on direct extraction from the user story field (explicit) and extraction according to implicit semantics (implicit). A multi-strategy prompting method (MSPM) is devised, which combines prompt templates, few-shot learning, ReAct, and chain-of-thought techniques to suit explicit and implicit goal model concept extraction, ultimately generating iStar models. The experimental results show that the proposed method has an average F 1 scores of 97.9% and 92.7% for the nodes and relationships in the extraction of the goal model, and has advantages in the completeness of the extraction of the goal modeling element.

Manually extracting device knowledge from dense hardware manuals for requirements elicitation is a timeconsuming and error-prone bottleneck in embedded systems development. While Large Language Models (LLMs) offer automation potential, their direct application is unreliable for this high-precision task due to factual inaccuracies and sensitivity to prompt engineering. To address this, we propose a novel hybrid method that synergizes parameter-efficient tuning with the reasoning power of LLMs. Instead of directly tuning the LLM, our approach uses soft prompts to efficiently optimize a lightweight sentence encoder, transforming it into a domainaware semantic retriever. This specialized retriever then generates superior, context-rich prompts for a frozen LLM. Our optimization strategy features a multi-task contrastive loss to discern fine-grained semantics and a clustering-based exemplar selection process to ensure prompt diversity and relevance. On a real-world dataset of devices, our method achieves F1-scores of 87.02% for entity extraction and 77.60% for relation extraction, significantly outperforming baseline LLMs and a strong, heavyweight, domain-specific pre-trained model (EquipBERT).

Modern software systems often involve diverse requirements from multiple stakeholders, leading to potential inconsistencies such as terminological ambiguities, contextual contradictions, and logical conflicts. Identifying these inconsistencies is critical for requirements engineering, but remains challenging due to the need for context-aware semantic analysis across diversity specifications. This paper proposes a hybrid framework that combines large-language models (LLMs) and graph-based merging algorithm to address this challenge. Our methodology first transforms textual requirements into graph-based models, explicitly capturing semantic relationships and contextual dependencies. A multi-graph merging algorithm then unifies these stakeholder-specific graphs into a merged representation, enabling systematic inconsistency detection through structural and semantic analysis. Using the contextual understanding capabilities of LLMs, the framework automates two phases: (1) disambiguation of natural language requirements during graph construction and (2) interpreting detected conflicts through explainable reasoning. We validate the framework through 3 case studies. Experimental results show the practical value of combining graph merging with LLM-powered semantic processing. This work advances requirements engineering by providing a scalable solution for inconsistency detection in diversity-intensive environments.

User stories are the main artifacts maintained in agile development and play an important role in describing requirements and making development plans. Writing good user stories becomes a key part of improving agile development. However, effective storytelling requires clear communication, reduced ambiguity, and enhanced testability. How to write good user stories is a challenge for beginners. In order to increase learner immersion and improve user story writing skills, this paper explores a gamification approach. Specifically, we started by creating specifications for beginners to write good user stories. Then, the process of user story writing is broken down, the gamification elements are refined, and the gamification teaching framework Story Explorer is proposed. In a course with 64 students, we assessed learning engagement and story quality. The results showed that the quality of user stories improved by an average of 11.8%, while students demonstrated a high level of engagement. Using Story Explorer significantly increased students' motivation to write high-quality user stories and the quality of their results.

User story is a widely adopted requirement notation in agile development.Generally, user stories are written by customers or users in natural language with limited form to describe user's needs for the software system from their perspectives.However, since user stories are generally presented in a flat list, the relations derived from the user stories are difficult to capture.It reduces the understanding of the system as a whole.One solution to this problem is to build goal-oriented models that provide explicit relations among user stories.But extracting concepts and relationships from a large number of discrete user stories often take a lot of time for the agile development team.This paper proposes an iStar model generating approach based on node-merging from user stories.The method first extracts the iStar nodes from the semi-structured user stories, then uses a BERT (Bidirectional Encoder Representations from Transformers) model to measure the similarity between the nodes, and then nodes to be merged are identified and the edges between the iStar nodes are connected.Experiments are designed to illustrate the effectiveness of the proposed approach.

OS-level virtualization is often used for server consolidation in data centers because of its high efficiency. However, the sharing of storage stack services among the colocated containers incurs contention on shared kernel data structures and locks within I/O stack, leading to severe performance degradation on manycore platforms incorporating fast storage technologies (e.g., SSDs based on nonvolatile memories). This article presents MultiLanes, a virtualized storage system for OS-level virtualization on manycores. MultiLanes builds an isolated I/O stack on top of a virtualized storage device for each container to eliminate contention on kernel data structures and locks between them, thus scaling them to manycores. Meanwhile, we propose a set of techniques to tune the overhead induced by storage-device virtualization to be negligible, and to scale the virtualized devices to manycores on the host, which itself scales poorly. To reduce the contention within each single container, we further propose SFS, which runs multiple file-system instances through the proposed virtualized storage devices, distributes all files under each directory among the underlying file-system instances, then stacks a unified namespace on top of them. The evaluation of our prototype system built for Linux container (LXC) on a 32-core machine with both a RAM disk and a modern flash-based SSD demonstrates that MultiLanes scales much better than Linux in micro- and macro-benchmarks, bringing significant performance improvements, and that MultiLanes with SFS can further reduce the contention within each single container.