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DTSTAMP:20260610T055057Z
DESCRIPTION: \n\nAbstract\n\nModel-Driven Software Engineering advocates th
 e use of models and their transformations across different stages of softw
 are engineering to better understand and analyze envisioned software syste
 ms. One of the most widely used models is Domain models (class diagrams) w
 hich capture the structural aspects of a system. During requirements analy
 sis or the early stages of design\, informal requirements written in natur
 al language are transformed into domain models which are analyzable and mo
 re concise. Since domain modelling is time-consuming\, labor-intensive\, a
 nd error-prone\, many approaches have been proposed to extract domain conc
 epts and relationships automatically. Despite the existing work on domain 
 model extraction\, some non-trivial challenges remain unaddressed: (i) the
  extracted domain models are not accurate enough to be used directly or wi
 th minor modifications in software development or for learning purposes\, 
 (ii) existing approaches do not facilitate interactions between a modeller
  and automated model extraction system\, and (iii) existing approaches do 
 not support traceability of automated modelling decisions.\n\nIn this thes
 is\, we present a domain modelling bot\, DoMoBOT\, that aims to support au
 tomated\, interactive\, and traceable domain modelling. First\, we present
  a layered architecture that uses Natural Language Processing (NLP) and Ma
 chine Learning (ML) techniques to extract domain models with accuracy high
 er than existing approaches. Second\, we extend the layered architecture t
 o discover alternative configurations and support bot-modeller interaction
 s. In addition\, we present a bot-assisted approach that aims to support m
 odellers during the evolution of domain modelling exercises. Moreover\, we
  present an incremental learning strategy that aims to learn a modeller’s 
 preferred decisions during bot-modeller interactions and improves the bot'
 s responses and extraction results over time. Third\, we present the Trace
 ability Information Model (TIM) that weaves various artifacts generated ac
 ross the model extraction process. We also report the evaluation results f
 rom case studies and a pilot user study to evaluate our extended layered a
 rchitecture\, bot-assisted approach\, and incremental learning strategy. F
 inally\, we present the technical details of the bot and discuss the resul
 ts of the summative user study where we evaluate our bot. We observe that:
  (1) a combination of NLP and ML techniques may extract model elements tha
 t are otherwise not possible or difficult with either of these techniques 
 alone\, (2) traceability of modelling decisions using TIM may assist model
 lers in gaining insights into the automated decisions taken by the bot\, (
 3) bot-modeller interactions can enable adaptability of extracted domain m
 odels for including modelling decisions preferred by a modeller\, and (4) 
 synergy between automated model extraction and bot-modeller interactions m
 ay better assist modellers in making coherent changes in problem descripti
 ons and their domain models.\n
DTSTART:20221123T153000Z
DTEND:20221123T173000Z
LOCATION:\, Room 603\, McConnell Engineering Building\, CA\, QC\, Montreal\
 , H3A 0E9\, 3480 rue University
SUMMARY:PhD defence of Rijul Saini - Automated\, Interactive\, and Traceabl
 e Domain Modelling
URL:/ece/channels/event/phd-defence-rijul-saini-automa
 ted-interactive-and-traceable-domain-modelling-343567
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