Studying the commonalities, mappings and relationships between non-functional requirements using machine learning

Alashqar, Abdelkareem M.

doi:10.1016/j.scico.2022.102806

Science of Computer Programming

2022

DOI: 10.1016/j.scico.2022.102806

|View full text |Cite

Studying the commonalities, mappings and relationships between non-functional requirements using machine learning

Abdelkareem M. Alashqar

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

Hassan,

Li,

Aurangzeb

et al. 2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Over the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.

show abstract

A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

Hassan,

Li,

Aurangzeb

et al. 2024

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

A Systematic Review of AI-Enabled Frameworks in Requirements Elicitation

Siddeshwar,

Alwidian,

Makrehchi

2024

IEEE Access

View full text Add to dashboard Cite

A Review of Non-Functional Requirements Analysis Throughout the SDLC

Dongmo

2024

Computers

View full text Add to dashboard Cite

To date, unquestionable efforts have been made, both in academia and industry, to facilitate the development of functional requirements (FRs) throughout the different phases of the software development life cycle (SDLC). Functional requirements are understood to mean the users’ needs pertaining to the services to be rendered by a software system. For example, semi-formal or graphically based approaches such as UML, and mathematically based or formal approaches such as Z and related tools have all been developed with the intention of addressing FRs. In the same vein, most of the proposed software methodologies, for instance, agile software development and model-driven software development, primarily target functional requirements. Considering the importance and even the criticality of non-functional requirements describing the quality of software systems and the constraints upon them, similar progress would be expected for their development. However, it appears that making headway with NFRs has been more challenging due to the complexity of the requirements. In this regard, the main purpose of this work is to unveil (from the academic perspective) the current state of development of NFRs through the review of publications carefully selected from five online databases.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Studying the commonalities, mappings and relationships between non-functional requirements using machine learning

Cited by 4 publications

References 15 publications

A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

A Systematic Review of AI-Enabled Frameworks in Requirements Elicitation

A Review of Non-Functional Requirements Analysis Throughout the SDLC

Contact Info

Product

Resources

About