International Conference on Computer and Knowledge Engineering

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Non-Functional Requirement Extracting Methods for AI-based Systems: A Survey

Authors :

Reza Damirchi¹ Amineh Amini²

1- Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran 2- Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

Keywords :

NFR،Requirement،Non-Functional،Quality،AI،Artificial Intelligence،Requirements Engineering،RE4AI

Abstract :

Artificial intelligence (AI) systems are growing rapidly in a variety of fields and have become a part of everyday human life. Traditional software development methods, especially in the requirements engineering domain, are not very efficient in the development of AI-based Systems. Given that the elicitation and analysis of software project requirements is the key to producing a high-quality and efficient product, there must be new approaches to identifying and extracting requirements in the field of AI-based systems. This is done in the research area of Requirements Engineering for Artificial Intelligence (RE4AI). Non-functional requirements are defined in accordance with the system's objectives and operational requirements. They specify the behaviors, limitations, and overall quality of the system; for this reason, they are very important for the success of the system, and lack of attention or neglect to accurately identify it will increase the probability of project failure. In this Paper, we aim to provide a comprehensive overview of the approaches and techniques for extracting non-functional requirements for AI-based systems by reviewing recent papers in this field. The review of these studies shows that there are currently no accurate methods for identifying and extracting non-functional requirements of AI-based Systems, but by relying on traditional methods and redefining their concepts, most of these methods can also be used in this domain. Additionally, new requirements for the field of AI have been introduced that are of high importance.

Papers List

List of archived papers

Spatial-channel attention-based stochastic neighboring embedding pooling and long short term memory for lung nodules classification

AHMED SAIHOOD - HOSSEIN KARSHENAS - AHMADREZA NAGHSH NILCHI

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HiCAP: Hierarchical Clustering-based Attention Pooling for Graph Representation Learning

Parsa Haddadian - Rooholah Abedian - Ali Moeini

An Improved and Accurate Measure for Mining Correlated High-utility Itemsets

Amir Masoud Heidari Orojloo - Morteza Keshtkaran

Link Prediction for Recommendation based on Complex Representation of Items Similarities

Masoumeh Alinia - Seyed Mohammad Hossein Hasheminejad - Hadi Shakibian

Robust Learning to Learn Graph Topologies

Navid Akhavan Attar - Ali Fahim

BERT transformers Multitask learning Sarcasm and Sentiment classification (BMSS)

Fatemeh Molavi - Jamshid Bagherzadeh Mohasefi

A Systematic Embedded Software Design Flow for Robotic Applications

Navid Mahdian - Seyed-Hosein Attarzadeh-Niaki - Armin Salimi-Badr

Deep Learning Feature Extraction for COVID-19 Detection Algorithm using Computerized Tomography Scan

Maisarah Mohd Sufian - Ervin Gubin Moung - Chong Joon Hou - Ali Farzamnia

Multi-Layered Defense Against Modern Phishing: A Dual-Sandbox and CDR Approach

Mahdi Seyfipoor - Mohammad Mahdi Eskandari