Dialogue Systems Engineering: A Survey and Future Directions

2025-08-04

AI Summary

Large language model–driven conversational systems face significant engineering challenges in scaling across social and commercial applications. Method: This paper pioneers the establishment of “Conversational Systems Engineering” (CSE) as a formal knowledge domain, grounded in the SWEBOK 4.0 framework, and systematically maps its end-to-end technical lifecycle via systematic literature review and domain analysis—identifying critical research gaps in requirements modeling, evaluation & validation, and continuous operations. Contribution/Results: First, it formally positions CSE as an emerging subfield of software engineering. Second, it introduces the first structured, extensible knowledge classification framework for conversational systems. Third, it delineates dedicated engineering methodologies tailored to conversational AI. Collectively, these contributions provide a theoretical foundation for academic research and a methodological roadmap for industrial practice.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.

Problem

• Defining and surveying Dialogue Systems Engineering field
• Applying dialogue systems to societal and business issues
• Evolving software engineering for dialogue systems improvement

Innovation

• Applying software engineering to dialogue systems
• Evolving tailored software engineering methods
• Surveying knowledge areas from SWEBOK 4.0