The emergence of large language models and conversational front-ends such as ChatGPT is revolutionizing many software engineering activities. The extent to which such technologies can help with requirements engineering activities, especially the ones surrounding modeling, however, remains to be seen. This paper reports on early experimental results on the potential use of GPT-4 in the latter context, with a focus on the development of goal-oriented models. We first explore GPT-4's current knowledge and mastering of a specific modeling language, namely the Goal-oriented Requirement Language (GRL). We then use four combinations of prompts – with and without a proposed textual syntax, and with and without contextual domain knowledge – to guide the creation of GRL models for two case studies. The first case study focuses on a well-documented topic in the goal modeling community (Kids Help Phone), whereas the second one explores a context for which, to our knowledge, no public goal models currently exist (Social Housing). We explore the interactive construction of a goal model through specific follow-up prompts aimed to fix model issues and expand on the model content. Our results suggest that GPT-4 preserves considerable knowledge on goal modeling, and although many elements generated by GPT-4 are generic, reflecting what is already in the prompt, or even incorrect, there is value in getting exposed to the generated concepts, many of which being non-obvious to stakeholders outside the domain. Furthermore, aggregating results from multiple runs yields a far better outcome than from any individual run. 

On the Use of GPT-4 for Creating Goal Models: An Exploratory Study

Providing a common data model for the metadata of several heterogeneous genomic data sources is hard, as they do not share any standard or agreed practice for metadata description. Two years ago we managed to discover a subset of common metadata present in most sources and to organize it as a smart genomic conceptual model (GCM); the model has been instrumental to our efforts in the development of a major software pipeline for data integration.

/pdf/exploiting-conceptual-modeling-for-searching-genomic-45k4e46tya.pdf

Exploiting Conceptual Modeling for Searching Genomic Metadata: A Quantitative and Qualitative Empirical Study

Bridging the gap between academia and industry is an important issue to promote the practicality of i* framework Researchers have been dealing with this issue from various perspectives, such as simplifying the meta-models or modeling processes of i* framework In this paper, we exclusively focus on the scalability issue in laying out large-scale i* models and propose a two-level layout approach to automatically lay out i* models in an efficient and comprehensible manner, contributing to the adoption of i* framework in the industry The proposed approach is designed by considering the semantics of i* constructs and layout conventions of i* models in order to produce meaningful layouts and can appropriately handle both the SD (Strategic Dependency) view and the SR (Strategic Rationale) view of i* models We have implemented our approach in an open-access prototype tool, which is able to be integrated with existing iStarML-compatible modeling tools We have conducted a controlled experiment, a case study, and performance testing to empirically and comprehensively evaluate the utility of our approach, the results of which show that our proposal can efficiently produce meaningful layouts that are as comprehensible as manually laid out models in most cases

Toward practical adoption of i* framework: an automatic two-level layout approach

Modeling of natural language requirements, especially for a large system, can take a significant amount of effort and time. Many automated model-driven approaches partially address this problem. However, the application of state-of-the-art neural network architectures to automated model element identification tasks has not been studied. In this paper, we perform an empirical study on automatic model elements identification for component state transition models from use case documents. We analyzed four different neural network architectures: feed forward neural network, convolutional neural network, recurrent neural network (RNN) with long short-term memory, and RNN with gated recurrent unit (GRU), and the trade-offs among them using six use case documents. We analyzed the effect of factors such as types of splitting, types of predictions, types of designs, and types of annotations on performance of neural networks. The results of neural networks on the test and unseen data showed that RNN with GRU is the most effective neural network architecture. However, the factors that result in effective predictions of neural networks are dependent on the type of the model element.

Model elements identification using neural networks: a comprehensive study

Goal modeling plays an imperative role in early requirements engineering, which has been investigated for decades. There have been many studies that show the usefulness of requirements goal models. However, the establishment of goal models is typically done manually, which is time-consuming and has a steep learning curve. In this paper, we propose a semi-automatic framework for constructing iStar models, which is a well-known goal modeling language. Specifically, we first investigate the practical needs of iStar modelers on the automation of iStar modeling by holding interviews, based on which we propose an interactive and iterative modeling process. Our proposal takes advantage of human decisions and artificial intelligence algorithms, respectively, aiming at achieving low modeling costs while maintaining the quality of models. We then propose a hybrid approach for automatically extracting goal model snippets from requirements text, which implements the automatic tasks of our proposed process. The proposed method combines logical reasoning with deep learning techniques so as to unleash the power of domain knowledge to assist with automation tasks. We have performed a series of experiments for evaluation. The experimental results show that our method achieves the F1-measure of 90.34% for actor entity extraction, 93.14% for intention entity extraction, and 83.18% for actor relation extraction, which can efficiently establish high-quality goal models. The artifacts are available at Zenodo1.

/pdf/assisting-in-requirements-goal-modeling-a-hybrid-approach-7rij83wd.pdf

Assisting in requirements goal modeling: a hybrid approach based on machine learning and logical reasoning

[Context] Establishing requirements models is an effective way to analyze them, which is typically dealt with in a graphical manner (i.e., the drag-and-draw fashion). However, as the size of models increases, the scalability issue has become an unignorable challenge, hindering the practical adoption of requirements modeling approach. Some researchers have recently proposed and promoted textual modeling approaches, mitigating these issues of requirements modeling. [Objective] In this paper, we aim at evaluating the two modeling methods, i.e., a graphical modeling method VS. a textual modeling method. In particular, we apply these two methods to iStar modeling language, which has been widely recognized as an effective means to model and analyze requirements. [Methods] We have systematically designed and conducted a controlled experiment with 38 participants to compare two iStar modeling methods (graphical and textual) using two corresponding modeling tools (piStar and T-Star). The experimental results reveal that the numbers of iStar model nodes and relationships built by the participants had no significant difference, regardless of the modeling method adopted. [Conclusions] First, the results show that the textual modeling method is as usable as the graphical modeling method when creating iStar models. Second, we have identified a number of issues that contribute to improving the utility and practicality of the iStar modeling method.

Graphical Modeling VS. Textual Modeling: An Experimental Comparison Based on iStar Models

iStar framework is an effective means for modeling and analyzing goals and interactions among social agents. Most of existing iStar modeling tools build iStar models via a graphical manner, which has a steep learning curve and suffer from scalability issues. Especially when dealing with large-scale models in industrial settings, it is very time consuming to play with the layout of models. In this paper, we present a tool which allows users to build iStar models from textual descriptions of systems. And the established models are then visualized with reasonable layouts. In particular, our tool supports both SD (Strategic Dependency) view and SR (Strategic Rationale) view of iStar models.

T-Star: A Text-Based iStar Modeling Tool

Being able to model and analyze social dependencies is one of the most important feature of iStar modeling language. However, typical layout algorithms (e.g., hierarchical layout and circular layout) are not suitable for laying out iStar models, especially, strategic dependency models. As a result, iStar models are typically constructed and laid out in a manual way, which is time-consuming and tedious, especially when dealing with large-scale models. In this paper, we present a prototype tool which can automatically lay out iStar model in a meaningful way by taking into account the semantics of iStar model elements. In particular, we have developed and integrated an istarml parser with our tool, enabling automatic layout of iStar models that are specified in terms of istarml. We believe our tool would be particularly interesting for people who used to deal with large-scale iStar models.

A Prototype for Generating Meaningful Layout of iStar Models

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