MUCE: a multilingual use case model extractor using GPT-3

Bajaj, Deepali; Goel, Anita; Gupta, Sunny; Batra, Hunar

doi:10.1007/s41870-022-00884-2

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…This key feature enables GPT-3 to predict the next word in a sentence sequence adeptly. It consists of two main components: an encoder and a decoder [23]. The encoder accepts the preceding word in a sentence and trans gures it into a vector format, thereby capturing its contextual essence.…”

Section: Generative Pre-trained Transformer-3 (Gpt-3) Modelmentioning

confidence: 99%

“…Conversely, the remaining engines offer powerful yet economical alternatives. The Curie engine manages complex tasks requiring advanced knowledge; Babbage stands out in performing speci c operations, while Ada is ideally suited for simpler, more straightforward tasks [23]. The Davinci engine is the most tting choice for our tool, ClassDiagGen, which diligently analyse complex text to identify classes, attributes, methods, and relationships.…”

Section: Generative Pre-trained Transformer-3 (Gpt-3) Modelmentioning

confidence: 99%

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ClassDiagGen Tool: Fine-Tuning the GPT-3 Model for Auto- mated Class Diagram Generation from Textual Descriptions

Altawaiha,

Al-Hgaish

2024

Preprint

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In the continually evolving realm of software engineering, the advent of Artificial Intelligence (AI) and its implications for automating traditionally laborious tasks has been of paramount interest. This study employs the GPT-3 model, a transformative AI architecture, in automating the extraction of class diagram elements from textual software requirements - a critical yet often complex task in object-oriented programming. GPT-3 was equipped to execute this task proficiently through model fine-tuning using tailored case studies. Our approach emphasized the few-shot learning technique, a proven effective method in enhancing the model's proficiency in specialized tasks. The developed tool, ClassDiagGen, was subjected to thorough testing and evaluation, showcasing exemplary performance with average precision and recall scores of 98.6% and 93.3%, respectively. Our findings underscore the profound potential of AI, particularly the GPT-3 model, in streamlining software development processes while highlighting the importance of customized model training. This study marks the beginning of an exciting journey, with the software engineering landscape poised for further transformative changes through AI integration.

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Section: Generative Pre-trained Transformer-3 (Gpt-3) Modelmentioning

confidence: 99%

Section: Generative Pre-trained Transformer-3 (Gpt-3) Modelmentioning

confidence: 99%

ClassDiagGen Tool: Fine-Tuning the GPT-3 Model for Auto- mated Class Diagram Generation from Textual Descriptions

Altawaiha,

Al-Hgaish

2024

Preprint

View full text Add to dashboard Cite

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“…Its critical advantage is its measure, it contains around 175 billion parameters and is 100 times bigger than GPT-2. It is prepared upon a 500billion-word information set i.e., Common Crawl collected from the web and available repositories [24]. The contrast between the three GPT models is their measure.…”

Section: Gpt-3mentioning

confidence: 99%

“…GPT-1 received the estimate and with GPT-2 the number of parameters was improved to 1.5 billion. With GPT-3, the number of parameters was boosted to 175 billion, making it the biggest artificial neural network [24,25]. (Shown in Table 1) (Table 2 and 3).…”

Section: Gpt-3mentioning

confidence: 99%

MARGEN: Marathi Question Answering Generative Conversation Model

Bhalshankar¹,

Deshmukh²

2023

Advances in Computer Science Research

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The conversational system aka chatbot market capture was worth USD 526 million in 2021 around the world. The innovations created such as Machine learning, Deep learning, Natural Language Processing (NLP), and Big data analytics have given a modern speed-quickening fuel to Artificial Intelligence. Well, a generative chatbot could be an exceptionally effective and shrewd conversational system as distant as its learning component is concerned. They can be trained from scratch like a newborn child by using Deep Learning techniques. GPT-1,2,3 is well known for English Language NLP tasks whereas IndicBART is also moving ahead for 11 Indian Languages for the NLU tasks. With the DL techniques like RNN, LSTM, and SGD, we conducted an in-depth survey of recent deep learning conversational models available on open-source portals, examining over 25 deep learning models for conversational systems before proposing our model. "MARGEN" is a proposed model which means the user can get the system's reply with proper generative answers for the query in Marathi text and/or audio speech formats.

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“…We assume that use case models are fundamental artifacts of any object-oriented system and are readily available to system designers. Use case set, UC A , can be gathered manually from the requirement documents or can be automated by parsing the given software requirements using NLP based language models as discussed in [44].…”

Section: B Proposed Approachmentioning

confidence: 99%

GreenMicro: Identifying Microservices From Use Cases in Greenfield Development

2022

Self Cite

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Microservices architecture is a new paradigm for developing a software system as a collection of independent services that communicate through lightweight protocols. In greenfield development, identifying the microservices is not a trivial task, as there is no legacy code lying around and no old development to start with. Thus, identification of microservices from requirements becomes an important decision during the analysis and design phase. Use cases play a vital role in the requirements analysis modeling phases in a model-driven software engineering process. It is a technique of capturing high-level user functions and scope of the system. In this paper, we propose GreenMicro, an automatic microservice identification technique that utilizes the use cases model and the database entities. Both features are the artifacts of analysis and design phase that depict complete functionality of an overall system. In essence, a collection of related use cases indicates a bounded context of the system that can be grouped in a suitable way as microservices. Therefore, our approach GreenMicro clusters close-knit use cases to recover meaningful microservices. We investigate and validate our approach on an in-house proprietary web application and three sample benchmark applications. We have mapped our approach to state-of-the-art software quality assessment attributes and have presented the results. Preliminary results are motivating and the proposed methodology works as anticipated in producing functionally cohesive and loosely coupled microservice candidate recommendations. Our approach enables the system architects to identify microservice candidates at an early analysis and design phase of development.

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MUCE: a multilingual use case model extractor using GPT-3

Cited by 15 publications

References 14 publications

ClassDiagGen Tool: Fine-Tuning the GPT-3 Model for Auto- mated Class Diagram Generation from Textual Descriptions

ClassDiagGen Tool: Fine-Tuning the GPT-3 Model for Auto- mated Class Diagram Generation from Textual Descriptions

MARGEN: Marathi Question Answering Generative Conversation Model

GreenMicro: Identifying Microservices From Use Cases in Greenfield Development

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