A Fuzzy Approach to Requirements Prioritization

Lima, Dayvison Chaves; Freitas, Fabrício Gomes de; Campos, Gutavo; Souza, Jerffeson

doi:10.1007/978-3-642-23716-4_8

Cited by 24 publications

(11 citation statements)

References 4 publications

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“…In a similar way, DRank (Shao, et al, 2017), Fuzzy AHP (Lima, et al, 2011) and Hierarchy AHP (Karlsson, et al, 1998) methods utilize AHP and machine learning techniques in order to reduce the required number of pairwise comparisons. DRank presents improvements with respect to CBRank, considering the stakeholders' preferences and dependencies between requirements.…”

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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Section: Related Workmentioning

confidence: 99%

“…In [47], researchers applied fuzzy logic for the prioritization of test cases which minimizes the cost of regression testing. Figure 6 represents the extraction of different solutions/activities when fuzzy logic is applied in different phases of the system development life cycle (SDLC) [48][49][50][51][52][53][54][55][56]. In the literature, different techniques for gathering requirements have been introduced by researchers [57][58][59][60], such as one-on-one interviews, group interviews, facilitated sessions, joint application design (JAD), a group support system (GSS), prototyping, questionnaires, use cases, following people around, requests for proposals (RFPs) and brainstorming.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic Testing Approach for Measuring Software Completeness

Iqbal

Sang

2021

Symmetry

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Due to advancements in science and technology, software is constantly evolving. To adapt to newly demanded requirements in a piece of software, software components are modified or developed. Measuring software completeness has been a challenging task for software companies. The uncertain and imprecise intrinsic relationships within software components have been unaddressed by researchers during the validation process. In this study, we introduced a new fuzzy logic testing approach for measuring the completeness of software. We measured the fuzzy membership value for each software component by a fuzzy logic testing approach called the fuzzy test. For each software component, the system response was tested by identifying which software components in the system required changes. Based on the measured fuzzy membership values for each software component, software completeness was calculated. The introduced approach scales the software completeness between zero and one. A software component with a complete membership value indicates that the software component does not require any modification. A non-membership value specifies that the existing software component is no longer required in the system or that a new software component is required to replace it. The partial membership value specifies that the software component requires few new functionalities according to the new software requirements. Software with a partial membership value requires partial restructuring and design recovery of its components. Symmetric design of software components reduces the complexity in the restructuring of software during modification. In the study, we showed that by using the introduced approach, high-quality software that is faultless, reliable, easily maintained, efficient, and cost-effective can be developed.

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“…The Fuzzy AHP method involves the use of normalized triangular fuzzy weights to rank requirements [44]. This technique is however not scalable and it does not give room for requirements interdependencies [45].…”

Section: Fuzzy Ahpmentioning

confidence: 99%

An Appraisal of Software Requirement Prioritization Techniques

Iroju¹,

Ikono

Gambo

2018

AJRCoS

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In recent times, there is a paradigm shift from the use of paper based systems to the use of software systems in all spheres of life. However, the development of high quality, cost effective and useable software systems is a major challenge. One of the major obstacles confronting the successful implementation of software systems is the inability to implement all stakeholders' requirements in software development projects. This constraint is usually due to limited human resources, budget and time. Thus, most software systems have failed. It, therefore, becomes pertinent to prioritize software requirements. Requirement prioritization involves the selection of requirements that are considered more important from an accumulated list of stakeholders' requirements. There are two techniques that are used for categorizing software requirements. These techniques include the requirement prioritization methods and the negotiation methods. Requirement prioritization methods are based on different scales which include nominal scale, ordinal scale and ratio scale. The accuracy of these methods, however, is a challenge especially when prioritizing large number of requirements. Aims: Hence, this paper reviews different techniques for prioritizing requirements by highlighting their strengths and weaknesses. Techniques such as binary search tree, AHP, hierarchy AHP, priority group/Numerical Analysis, bubble sort, MoSoW, simple ranking and Planning Game were analyzed and compared in this study. Methodology: The study is based on previous literature on requirement prioritization. Results: The study showed that numerical assignment and simple ranking methods require less time in the prioritization process and they also have low scalability and reliability. The study also showed that the analytic hierarchy process requires more time for requirement prioritization; it is reliable but it is not scalable. The study also revealed that it is difficult to prioritize requirements with the existing prioritization techniques when multiple stakeholders are involved. Conclusion: The study suggests that future researches should be based on the design of requirement prioritization techniques that will have the ability to accommodate large stakeholders and requirements.

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A Fuzzy Approach to Requirements Prioritization

Cited by 24 publications

References 4 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

Fuzzy Logic Testing Approach for Measuring Software Completeness

An Appraisal of Software Requirement Prioritization Techniques

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