Requirements Prioritization Techniques Comparison

Hudaib, Amjad; Masadeh, Raja; Qasem, Mais Haj; Alzaqebah, Abdullah

doi:10.5539/mas.v12n2p62

Cited by 53 publications

(29 citation statements)

References 18 publications

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“…All in all, one of the most common limitations of the methods mentioned above (AHP, PAHPT, Cost-Value, Pairwise Analysis, CBRank, DRank, Fuzzy AHP, Hierarchy AHP, IGA and Cognitive-Driven) is that they do not specifically address requirements collisions throughout the prioritization process. In fact, they turn out to be time-consuming for large sets of requirements, being hardly scalable (Karlsson, et al, 1998;Hudaib, et al, 2018) in the long run as well.…”

Section: Related Workmentioning

confidence: 99%

Toward collisions produced in requirements rankings: A qualitative approach and experimental study

Rojas

Macías

2019

Journal of Systems and Software

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Requirements prioritization is an important issue that determines the way requirements are selected and processed in software projects. There already exist specific methods to classify and prioritize requirements, most of them based on quantitative measures. However, most of existing approaches do not consider collisions, which are an important concern in large-scale requirements sets and, more specifically, in agile development processes where requirements have to be uniquely selected for each software increment. In this paper, we propose QMPSR (Qualitative Method for Prioritizing Software Requirements), an approach that features the prioritization of requirements by considering qualitative elements that are related to the project's priorities. Our approach highlights a prioritization method that has proven to reduce collisions in software requirements rankings. Furthermore, QMPSR improves accuracy in classification when facing large-scale requirements sets, featuring no scalability problems as the number of requirements increases. We formally introduce QMPSR and then define prioritization effort and collision metrics to carry out comprehensive experiments involving different sets of requirements, comparing our approach with well-known existing prioritization methods. The experiments have provided satisfactory results, overcoming existing approaches and ensuring scalability.

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Section: Related Workmentioning

confidence: 99%

Toward collisions produced in requirements rankings: A qualitative approach and experimental study

Rojas

Macías

2019

Journal of Systems and Software

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“…Requirements prioritization is seen as one of the most significant tasks for decision-makers (leaders or managers) within a project [3]. On the other hand, Hudaib et al [4] treats it as the primary process in software engineering, providing the perfect order of implementation of the requirements to plan software versions and provide desirable functionality to customers.…”

Section: Requirement Prioritizationmentioning

confidence: 99%

Investigating Fitness Functions for Search-based Requirements Prioritization

Corezolla

Costa

Souza

et al. 2021

Anais Do XII Computer on the Beach - COTB '21

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It can be challenging for people to select the most relevant requirementamong several software system development options.Requirements prioritization defines the ordering for executing requirementsbased on their priority or importance concerning stakeholders’viewpoints, which is a problematic task. Based on thisproblem, this study aims to present a requirements prioritizationapproach using a genetic algorithm to find optimal solutions, andit can assist in the requirements prioritization activity during thesoftware development process. In this paper, we investigated aset of criteria to create four functions GUT-D, ThS-D, ST, and LT,to assess candidate solutions, i.e., the recommended prioritizedrequirements. We examine the empirical results concerning thepractical approach’s effectiveness and cost computational two experimentsin the evaluation. We found that the 𝐺𝑈𝑇 − 𝐷 fitnessfunction achieved the best fitness value in different settings with90.51% and 98.63%. Besides that, our study demonstrates that the approachis promising to assist requirements prioritization since eachfitness function can be used individually according to companies’necessities.

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“…• Coverage: All user scenarios and requirements have to be covered, considering the system-level importance as it is proposed in the MoSCoW technique 63 . Based on the MoSCoW mechanism, system requirements specifications (SRS) are categorized in nominal order according to their priority (must, should, could and won't have), which most of the time illustrates the user's needs.…”

Section: System Specifications: a Co-design Processmentioning

confidence: 99%

An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives

Karagiannopoulou¹,

Tsiakos²,

Tsimiklis³

et al. 2020

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII

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The EU-funded DIONE project (grant agreement No. 870378) offers an innovative close-to-market (TRL7) solution seeking to improve the traditional methods of agricultural monitoring. The project introduces a cloud-based Software as a Service (SaaS) system architecture, building on a fusion of novel technologies that will support the forthcoming needs of the modernized Common Agriculture Policy (CAP) and the "Greening" perspectives, with an automated area-based monitoring system. In particular, an interoperable and harmonized system is designed, connecting large volumes of Earth Observation data (Satellite, UAV, and in-situ) and user-generated highly precise geolocated data (geo-tagged photos, soil measurements, etc.). DIONE's system architecture encompasses customized and third-party frameworks, where heterogeneous and multi-source data are stored, processed and managed using Artificial Intelligence (AI) algorithms. These harmonized, curated and open accessed data are then provided as Open Geospatial Consortium (OGC)-compliant, web-service layers (WMS, WFS, and WCS). Furthermore, the proposed solution formulates a scalable, flexible, interoperable, and semantically enriched environment, taking advantage of a Spatial Data Infrastructure (SDI) framework capabilities, whilst allowing an interactive connection among different tools and components through RESTful APIs. Our approach establishes a novel, cloud-based, accurate and inexpensive agriculture monitoring solution, enabling the real-time provision of multi-source data to relevant stakeholders such as Paying Agencies, Policy Officers and Control & Certification Bodies, and other domain experts. The system architecture was formulated exploiting a codesign methodology, aiming to ensure a long-term and sustainable solution. Two large scale demonstration will take place in Lithuania and Cyprus, evaluating the system capabilities in real-life and operational conditions.

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Requirements Prioritization Techniques Comparison

Cited by 53 publications

References 18 publications

Toward collisions produced in requirements rankings: A qualitative approach and experimental study

Toward collisions produced in requirements rankings: A qualitative approach and experimental study

Investigating Fitness Functions for Search-based Requirements Prioritization

An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives

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