Dataset for Heuristic-Based Incremental Local Domain Model Generation

Abstract

Dataset used in the evaluation of the paper "Heuristic-Based Incremental Local Domain Model Generation", currently under review in "Information and Software Technology".

Context: Current front-end frameworks and technologies enable rich clients to operate autonomously without frequent server requests. To achieve this autonomy, clients must maintain a Local Domain Model (LDM), often derived from the Global Domain Model (GDM) on the backend. Manually designing an LDM that is consistent with the GDM requires handling nuanced dependencies, an error-prone task where oversight is easy. Objective: To address these challenges we aim to: (a) formally map dependencies between GDM and LDM; (b) analyze effort and errors when modelling without assistance; and (c) provide a semi-automated method leveraging these dependencies to significantly reduce both effort and errors. Method: To achieve these objectives, we propose a heuristic-based, step-by-step guided method. This approach leverages pre-existing GDM information to incrementally uncover dependencies and automate LDM construction as designers identify local behavior of GDM elements. We assessed this method's impact through an empirical experiment where we aimed to identify common mistakes and quantify effort during LDM construction. Expert UML modelers completed an LDM creation task both manually and with our tool-supported method. We recorded errors and cognitive effort to establish a baseline and measure impact. User perceptions were gathered via a survey; an analytical usability study based on GOMS complemented findings. Results: The proportion of users committing errors decreased by 77.8% with the tool, and the average error count per user was reduced by 97.3%. Time to complete the task decreased by 35.0% and interactive effort by 44.6%, consistent with GOMS predictions. Surveys showed majority positive responses across all items. Conclusions: Our approach effectively streamlines Local Domain Model creation. By automatically detecting dependencies and guiding designers, the tool drastically reduces error rates, cuts completion time, and lowers interaction volume. Expert users rated the method positively, affirming that benefits of guided, incremental LDM construction outweigh adoption effort.