Selection of Requirement Elicitation Techniques: A Neural Network based Approach

Muqeem, Mohd; Ahmad, Sultan; Nazeer, Jabeen; Farooqui, Md. Faizan; Alam, Afroj

doi:10.14569/ijacsa.2022.0130144

Cited by 3 publications

(5 citation statements)

References 40 publications

(51 reference statements)

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“…e kth sentence extraction result y k ∈ {0, 1} of the i-th pseudo-summary Plabel i is compared. Finally, without the intervention of the abstract truth value, the coherence measurement module is guided to obtain the relevant parameter information of key sentence extraction in advance [40].…”

Section: Key Statement Selection and Auxiliary Informationmentioning

confidence: 99%

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Abdeljaber

Ahmad

Alharbi

et al. 2022

Security and Communication Networks

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The purpose of automatic text summarising technology is to condense a given text while properly portraying the main information in the original text in a summary. To present generative text summarising approaches, on the other hand, restructure the original language and introduce new words when constructing summary sentences, which can easily lead to incoherence and poor readability. This research proposes a XAI (explainable artificial intelligence)-based Reinforcement Learning-based Text Summarization of Social IoT-Based Content using Reinforcement Learning. Furthermore, standard supervised training based on labelled data to improve the coherence of summary sentences has substantial data costs, which restricts practical applications. In order to do this, a ground-truth-dependent text summarization (generation) model (XAI-RL) is presented for coherence augmentation. On the one hand, based on the encoding result of the original text, a sentence extraction identifier is generated, and the screening process of the vital information of the original text is described. Following the establishment of the overall benefits of the two types of abstract writings, the self-judgment approach gradient assists the model in learning crucial sentence selection and decoding the selected key phrases, resulting in a summary text with high sentence coherence and good content quality. Experiments show that the proposed model's summary content index surpasses text summarising ways overall, even when there is no pre-annotated summary ground-truth; information redundancy, lexical originality, and abstract perplexity also outperform the current methods.

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Section: Key Statement Selection and Auxiliary Informationmentioning

confidence: 99%

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Abdeljaber

Ahmad

Alharbi

et al. 2022

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

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“…In [1] framework, clustering was added to enable sequential analysis and hierarchical analysis of sentences, which were then applied to ontology construction. By utilizing hierarchical analysis in ontology construction, it becomes possible to identify higher-level concepts of requirements between sentences.…”

Section: A System Configurationmentioning

confidence: 99%

“…The process of constructing the knowledge base data is described in [1] following the approach. The problem statement is preprocessed using Mecab and BERT Q&A to perform morphological analysis and sequential analysis of the sentences, respectively.…”

Section: B Mecab Morphological Analysis Bert Qanda Knowledge Basementioning

confidence: 99%

“…Insufficient requirements analysis can lead to project failures, while a proper requirements elicitation process serves as the foundation for overall software product quality. Therefore, it is necessary to perform requirements analysis in a thorough and specific manner, considering its importance and high impact on the overall success or failure of a project [1].…”

Section: Introductionmentioning

confidence: 99%

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Chatbot Program for Proposed Requirements in Korean Problem Specification Document

Baek,

Park,

Park

2023

IJACSA

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In software engineering, requirement analysis is a crucial task throughout the entire process and holds significant importance. However, factors contributing to the failure of requirement analysis include communication breakdowns, divergent interpretations of requirements, and inadequate execution of requirements. To address these issues, a proposed approach involves utilizing NLP machine learning within Korean requirement documents to generate knowledge-based data and deduce actors and actions using natural language processing knowledge-based information. Actors and actions derived are then structured into a hierarchy of sentences through clustering, establishing a conceptual hierarchy between sentences. This is transformed into ontology data, resulting in the ultimate requirement list. A chatbot system provides users with the derived system event list, generating requirement diagrams and specification documents. Users can refer to the chatbot system's outputs to extract requirements. In this paper, the feasibility of this approach is demonstrated by applying it to a case involving Korean-language requirements for course enrollment.

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“…Because changes are inevitable, it has also been a significant problem during the software development process (Sommerville & Sawyer, 2000). Requirements can be changed by adding new ones, removing old ones, or improving existing requirements [14]. Key contribution of this research is as below-: 1.…”

Section: Introductionmentioning

confidence: 99%

An AHP-Based Framework for Effective Requirement Management in Agile Software Development (ASD)

Kashif Asad

2023

IJRITCC

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In Agile changes may be made at any time throughout the project lifetime in agile projects. These changes, however, often lead to longer turnaround times and higher costs while having a substantial influence on the activities and quality of project management. The suggested framework's main goal is to increase the effectiveness and flexibility of the requirement engineering process by successfully managing requirements changes, particularly in contexts where Agile Software Development (ASD) is practiced. The Agile methodology has gained popularity as a strategy for developing software because it can adjust to changing requirements and deliver software gradually. To make sure that the software being produced satisfies stakeholder expectations and adds value to the firm, good requirement management is essential. Using the Analytic Hierarchy Process (AHP) to prioritize requirements based on their relative relevance and urgency, this article introduces a framework for requirement management in Agile Projects. The most important requirements are taken care of first thanks to this strategy, which enables a more organized and informed decision-making process. We demonstrate the actual use of our framework in real-world contexts and highlight its efficacy in solving the issues faced by Agile projects by including a case study and an accompanying table. The suggested framework also supports the three core tenets of the Agile approach—transparency, cooperation, and continuous improvement—to foster an environment of excellence and ongoing learning within the Agile team. By developing these fundamental ideas, our framework not only supports Agile teams' continual growth and development but also helps them manage and prioritize requirements more efficiently, which ultimately improves project results and increases organizational value.

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Selection of Requirement Elicitation Techniques: A Neural Network based Approach

Cited by 3 publications

References 40 publications

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

XAI-Based Reinforcement Learning Approach for Text Summarization of Social IoT-Based Content

Chatbot Program for Proposed Requirements in Korean Problem Specification Document

An AHP-Based Framework for Effective Requirement Management in Agile Software Development (ASD)

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