A systematic literature review on requirement prioritization techniques and their empirical evaluation

Faiza Allah Bukhsh, Zaharah Allah Bukhsh, Maya Daneva*

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

Abstract

[Context and Motivation] Many requirements prioritization approaches have been proposed, however not all of them have been investigated empirically in real-life settings. As a result, our knowledge of their applicability and actual use is incomplete.

[Question/problem] A 2007 systematic review on requirements prioritization mapped out the landscape of proposed prioritization approaches and their prioritization criteria. To understand how this sub-field of requirements engineering has developed since 2007 and what evidence has been accumulated through empirical evaluations, we carried out a literature review that takes as input publications published between 2007 and 2019.

[Principle ideas/results] We evaluated 102 papers that proposed and/or evaluated requirements prioritization methods. Our results show that the newly proposed requirements prioritization methods tend to use as basis fuzzy logic and machine learning algorithms. We also concluded that the Analytical Hierarchy Process is the most accurate and extensively used requirement prioritization method in industry. However, scalability is still its major limitation when requirements are large in number. We have found that machine learning has shown potential to deal with this limitation. Last, we found that experiments were the most used research method to evaluate the various aspects of the proposed prioritization approaches.

[Contribution] This paper identified and evaluated requirements prioritization techniques proposed between 2007 and 2019, and derived some trends. Limitations of the proposals and implications for research and practice are identified as well.

Original languageEnglish
Article number103389
JournalComputer Standards and Interfaces
DOIs
Publication statusAccepted/In press - 1 Jan 2019

Fingerprint

Learning systems
Requirements engineering
evaluation
Learning algorithms
Fuzzy logic
Scalability
logic
learning
research method
engineering
Industry
industry
Experiments
experiment
trend
evidence
literature

Keywords

  • Empirical research method
  • Empirical study
  • Requirements engineering
  • Requirements prioritization
  • Systematic literature review

Cite this

@article{ff60c8aada7c45d59908d74d314c4661,
title = "A systematic literature review on requirement prioritization techniques and their empirical evaluation",
abstract = "[Context and Motivation] Many requirements prioritization approaches have been proposed, however not all of them have been investigated empirically in real-life settings. As a result, our knowledge of their applicability and actual use is incomplete.[Question/problem] A 2007 systematic review on requirements prioritization mapped out the landscape of proposed prioritization approaches and their prioritization criteria. To understand how this sub-field of requirements engineering has developed since 2007 and what evidence has been accumulated through empirical evaluations, we carried out a literature review that takes as input publications published between 2007 and 2019.[Principle ideas/results] We evaluated 102 papers that proposed and/or evaluated requirements prioritization methods. Our results show that the newly proposed requirements prioritization methods tend to use as basis fuzzy logic and machine learning algorithms. We also concluded that the Analytical Hierarchy Process is the most accurate and extensively used requirement prioritization method in industry. However, scalability is still its major limitation when requirements are large in number. We have found that machine learning has shown potential to deal with this limitation. Last, we found that experiments were the most used research method to evaluate the various aspects of the proposed prioritization approaches.[Contribution] This paper identified and evaluated requirements prioritization techniques proposed between 2007 and 2019, and derived some trends. Limitations of the proposals and implications for research and practice are identified as well.",
keywords = "Empirical research method, Empirical study, Requirements engineering, Requirements prioritization, Systematic literature review",
author = "Bukhsh, {Faiza Allah} and Bukhsh, {Zaharah Allah} and Maya Daneva",
year = "2019",
month = "1",
day = "1",
doi = "10.1016/j.csi.2019.103389",
language = "English",
journal = "Computer Standards & Interfaces",
issn = "0920-5489",
publisher = "Elsevier",

}

TY - JOUR

T1 - A systematic literature review on requirement prioritization techniques and their empirical evaluation

AU - Bukhsh, Faiza Allah

AU - Bukhsh, Zaharah Allah

AU - Daneva, Maya

PY - 2019/1/1

Y1 - 2019/1/1

N2 - [Context and Motivation] Many requirements prioritization approaches have been proposed, however not all of them have been investigated empirically in real-life settings. As a result, our knowledge of their applicability and actual use is incomplete.[Question/problem] A 2007 systematic review on requirements prioritization mapped out the landscape of proposed prioritization approaches and their prioritization criteria. To understand how this sub-field of requirements engineering has developed since 2007 and what evidence has been accumulated through empirical evaluations, we carried out a literature review that takes as input publications published between 2007 and 2019.[Principle ideas/results] We evaluated 102 papers that proposed and/or evaluated requirements prioritization methods. Our results show that the newly proposed requirements prioritization methods tend to use as basis fuzzy logic and machine learning algorithms. We also concluded that the Analytical Hierarchy Process is the most accurate and extensively used requirement prioritization method in industry. However, scalability is still its major limitation when requirements are large in number. We have found that machine learning has shown potential to deal with this limitation. Last, we found that experiments were the most used research method to evaluate the various aspects of the proposed prioritization approaches.[Contribution] This paper identified and evaluated requirements prioritization techniques proposed between 2007 and 2019, and derived some trends. Limitations of the proposals and implications for research and practice are identified as well.

AB - [Context and Motivation] Many requirements prioritization approaches have been proposed, however not all of them have been investigated empirically in real-life settings. As a result, our knowledge of their applicability and actual use is incomplete.[Question/problem] A 2007 systematic review on requirements prioritization mapped out the landscape of proposed prioritization approaches and their prioritization criteria. To understand how this sub-field of requirements engineering has developed since 2007 and what evidence has been accumulated through empirical evaluations, we carried out a literature review that takes as input publications published between 2007 and 2019.[Principle ideas/results] We evaluated 102 papers that proposed and/or evaluated requirements prioritization methods. Our results show that the newly proposed requirements prioritization methods tend to use as basis fuzzy logic and machine learning algorithms. We also concluded that the Analytical Hierarchy Process is the most accurate and extensively used requirement prioritization method in industry. However, scalability is still its major limitation when requirements are large in number. We have found that machine learning has shown potential to deal with this limitation. Last, we found that experiments were the most used research method to evaluate the various aspects of the proposed prioritization approaches.[Contribution] This paper identified and evaluated requirements prioritization techniques proposed between 2007 and 2019, and derived some trends. Limitations of the proposals and implications for research and practice are identified as well.

KW - Empirical research method

KW - Empirical study

KW - Requirements engineering

KW - Requirements prioritization

KW - Systematic literature review

UR - http://www.scopus.com/inward/record.url?scp=85076538445&partnerID=8YFLogxK

U2 - 10.1016/j.csi.2019.103389

DO - 10.1016/j.csi.2019.103389

M3 - Review article

AN - SCOPUS:85076538445

JO - Computer Standards & Interfaces

JF - Computer Standards & Interfaces

SN - 0920-5489

M1 - 103389

ER -