A Survey on Model‐Driven Engineering and Domain‐Specific Languages for Chatbot Development: Requirements, Challenges and Solutions
Bibliographic record
Abstract
ABSTRACT Chatbots have become widely adopted tools for improving user interactions across multiple platforms. They are advanced software applications designed to emulate human conversation across various platforms. Moreover, developing chatbots using existing platforms and frameworks presents challenges, such as the lock‐in of NLP services, and incurs substantial costs. Recently, research has introduced solutions to ease chatbot development. Many of these approaches utilise Model‐Driven Engineering (MDE) and Domain‐Specific Languages (DSLs) to automate processes and simplify implementation. Through the use of MDE and DSLs, these solutions enhance efficiency and make chatbot creation more accessible. This study aims to provide a comprehensive survey on MDE and DSLs in chatbot development, highlighting key research topics, opportunities, and challenges. The first contribution explores the primary application domains of DSLs in chatbot development and the associated challenges in their adoption. Second, this work examines the various ways in which DSLs are employed to model and develop chatbots, assessing their impact on automation and efficiency. Additionally, this study identifies the challenges and limitations of using DSLs in chatbot development. Atlast, it investigates the influence of DSL utilisation on user experience, both from the perspective of chatbot developers and end‐users, to determine how DSLs enhance the chatbot development process and interaction quality. To achieve this, a comprehensive search will be conducted across Scopus, Web of Science, and ScienceDirect for studies published between 2014 and 2024. A total of 306 publications were reviewed, of which 15 were identified as primary studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".