sales

Existing approaches to extracting Customer Needs (CNs) from unstructured textual data (e.g., user interviews, reviews) rely heavily on costly manual annotation, suffer from poor interpretability, and lack traceability. Method: We propose an end-to-end framework integrating supervised fine-tuning (SFT) of large language models (LLMs), domain-expert-annotated training data, structured prompt engineering, and collaborative evaluation with professional consulting firms. Contribution/Results: For the first time, blinded empirical evaluation demonstrates that our fine-tuned LLM matches or exceeds senior human analysts across four key dimensions—accuracy, specificity, traceability, and coverage completeness—while exhibiting no hallucination, strong business adaptability, and high interpretability. The method significantly improves both CN extraction efficiency and precision of innovation insights, establishing a reproducible, verifiable paradigm for automating requirements engineering.

This study addresses the unclear practical impact of generative AI in requirements engineering (RE) within current industrial practice, particularly regarding tool integration, team collaboration, and organizational adaptability. Drawing on a company-wide use case survey conducted in 2024 and two rounds of interviews with eight product owners during 2025–2026, the research systematically analyzes fifteen RE use cases across four categories, leveraging an in-house chatbot and seven commercial generative AI tools. Findings reveal that AI adoption has moved beyond individual productivity gains to influence complex scenarios such as cross-tool integration, customer governance responses, and role boundary reconfiguration. The degree of tool integration critically determines performance benefits, while single-user interaction modes may undermine collaborative dynamics. The study proposes a practitioner-oriented set of evaluation questions to guide effective industrial deployment of AI in RE.

This work proposes SalesCopilot, the first end-to-end real-time AI assistant designed to address the 25–65 second delays caused by sales representatives manually retrieving product information during customer calls—a bottleneck that severely degrades user experience and operational efficiency. SalesCopilot integrates streaming speech-to-text transcription, large language model (LLM)-driven query understanding, and retrieval-augmented generation (RAG) over a structured product database to deliver domain-agnostic, instant responses. Evaluated in an insurance sales setting, the system achieves an average response latency of 2.8 seconds with 100% query detection accuracy, yielding a 14-fold speedup over manual lookup and effectively eliminating the information-access bottleneck in live sales interactions.

This study addresses the ambiguity in defining the Research Software Engineer (RSE) role and the absence of standardized competency criteria. Employing a Delphi method combined with multi-institutional case studies—and integrating educational competency mapping with career development theory—it constructs the first cross-institutional, hierarchical, and scalable RSE competency framework. The framework innovatively proposes a four-dimensional competency model encompassing technical proficiency, collaborative practice, research engagement, and research ethics. It systematically delineates core responsibilities, foundational competencies, professional values, and career progression pathways for RSEs, supporting role evolution and professionalization. The resulting framework has been established as an internationally recognized competency benchmark, formally adopted by multiple national RSE associations for training and certification, and has driven curriculum reform in RSE-related programs across over ten universities worldwide.

Existing CRM evaluation benchmarks fail to capture real-world business complexity, hindering the integration and validation of AI agents in professional settings. Method: We introduce CRMArena—the first industrial-grade CRM workflow benchmark—featuring nine realistic tasks across three roles (service agent, analyst, manager), grounded in 16 highly interdependent object types and latent-variable modeling to encode intricate business logic and regulatory constraints. It uniquely integrates domain expert knowledge with latent-variable formalization and evaluates agents via dual paradigms: ReAct prompting and structured function calling, under high-fidelity object-relational modeling and dynamic data distribution simulation. Contribution/Results: CRMArena systematically exposes critical LLM agent limitations in rule adherence and structured function invocation. Experiments show state-of-the-art LLM agents achieve only 40% task completion under ReAct and 55% under function calling—highlighting the stringent demands of real-world CRM on robustness, compliance, and structured operational fidelity.

In complex organizations, product diversity, legacy systems, organizational inertia, and regulatory constraints severely impede the adoption of end-to-end Continuous Software Engineering (CSE). Method: Drawing on empirical studies across automation, automotive, retail, and chemical industries, this paper proposes an evolutionary CSE adoption pathway. It extends the CSE readiness model by introducing explicit internal and external feedback layers and distinguishing market constraints (e.g., compliance requirements) from organizational constraints (e.g., process rigidity), thereby enabling phased, context-sensitive implementation. The model is validated and refined through expert interviews and narrative synthesis. Contribution/Results: Results demonstrate that—even without achieving full-chain continuous delivery—prioritizing internal engineering capability enhancement significantly improves delivery efficiency and business responsiveness. The extended readiness model supports pragmatic, incremental CSE adoption in highly regulated, heterogeneous environments.

This study investigates software development teams’ awareness, attitudes, and readiness for organizational change prior to migrating to Software Product Line (SPL) engineering in Small and Medium-sized Enterprises (SMEs). Using semi-structured, in-depth interviews with key stakeholders across multiple roles, we conducted a qualitative study grounded in an SPL implementation framework and applied thematic analysis. Results indicate unanimous recognition of the migration’s strategic benefits, confirming the critical role of early stakeholder engagement in mitigating transition risks. Based on empirical findings, we propose a three-dimensional strategy to alleviate resistance to change: sustained cross-functional communication, incremental adoption of existing practices, and inclusive, collaborative implementation. This work addresses an empirical gap by systematically assessing pre-migration organizational cognition within SMEs—contextually distinct from large enterprises—and delivers actionable, context-sensitive change management guidance tailored for resource-constrained software organizations undertaking SPL adoption.

This study addresses the systematic neglect of non-technical requirements in software engineering recruitment, focusing on employers’ socio-technical expectations regarding candidates’ values, cultural fit, growth mindset, and collaborative competence. Applying thematic analysis—a rigorous qualitative methodology—we conducted systematic coding and category abstraction on 100 job descriptions for software engineers posted in 2023 across major Chinese online recruitment platforms. This constitutes the first empirical, theory-grounded characterization of soft skills and organizational expectations in this domain. Four core dimensions emerged: mission alignment, cultural integration, autonomous professional development, and collaborative communication—each empirically validated as critical criteria defining the “ideal candidate.” The findings provide actionable, evidence-based insights to guide curriculum reform in higher education, standardize industry hiring practices, and inform the construction of a comprehensive software engineering talent competency framework.

This study addresses the dynamic, modular, and diverse demands of vocational education by proposing a systematic approach to effectively integrate requirements engineering (RE) into practitioner-oriented software engineering curricula. Grounded in three real-world course development initiatives, the approach centers on curriculum content mapping and synergistically combines modular design principles with the established RE Body of Knowledge to construct an adaptable integration framework. This framework enables flexible yet structured incorporation of RE content, significantly enhancing curricular adaptability and instructional effectiveness. Empirical validation through the implemented courses demonstrates the feasibility and practical value of the proposed method in vocational education contexts, offering a scalable model for aligning software engineering education with industry needs.

This paper addresses the joint optimization of dynamic time-window assignment and service engineer routing in OEM after-sales service for high-tech equipment, considering customer preferences, service-level agreements, and real-time responsiveness. We propose DTSAP-CCP, a hierarchical sequential decision-making model. Methodologically, we integrate an attention-enhanced deep reinforcement learning (DRL) framework with a scenario-sampling-based planning architecture, incorporating a Rollout execution structure and training a neuro-heuristic solver to accelerate combinatorial path planning under uncertainty. Compared to conventional rule-based heuristics and standard Rollout baselines, our approach achieves significant improvements in both solution quality and computational efficiency. Empirical validation on large-scale medical equipment after-sales service operations demonstrates its effectiveness, robustness, and scalability in complex, real-world settings.