A Hybrid Approach for Requirements Prioritization Using Logarithmic Fuzzy Trapezoidal Approach (LFTA) and Artificial Neural Network (ANN)

Singh, Yash Veer; Kumar, Bijendra; Chand, Satish; Sharma, Deepak Kumar

doi:10.1007/978-981-13-3804-5_26

Cited by 7 publications

(12 citation statements)

References 25 publications

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“…Singh et. al in [59] propose a hybrid strategy that combines logarithmic fuzzy preference programming (LFPP) and artificial neural networks (ANN) to prioritize a customer's requirements based on numerous factors at a reasonable cost. Multiple criteria are entered as part of the information.…”

Section: ) Dabbagh and Lee (2015)mentioning

confidence: 99%

“…It has been designed to assess the techniques specifically in the light of the properties and attributes that a good fuzzy logic-based requirements ranking technique should have. A good fuzzy logic method should provide consistency ratio that should be always between 0 & 0.1 [59], membership function should always be positive [59], [64] and self-adaptive property [24], [30], [31], [37], [60].…”

Section: B Criteria For Fuzzy Logic Based Techniquesmentioning

confidence: 99%

“…It should not be negative for effective and desired results. Results show that out of 15 surveyed techniques, only two techniques [24] [35] show 90 % and 93.89% accuracy respectively due to multi-level prioritization, 9 techniques [44], [30], [31], [51], [61], [49], [59], [64], [36] are partially accurate whereas other studies do not mention neither provide any computation of level of disagreement. So this is evidence that the accuracy of fuzzy logic systems needs to be computed and improved.…”

Section: ) Strengths and Weaknesses Of Fuzzy Logic-based Techniquesmentioning

confidence: 99%

“…In terms of both time utilization and capability to learn, the Fuzzy Logic technique is more efficient, compared to related approaches other than techniques proposed in [30], [44], [59], [51], [60], [61], [64], [36] where researchers have not provided any detail about this parameter. We analyze that a certain level of automation leads to a more efficient technique in the end.…”

Section: ) Strengths and Weaknesses Of Fuzzy Logic-based Techniquesmentioning

confidence: 99%

“…Existing requirements prioritization techniques suffer from several problems such as complexity of application, expensive in computations, lack of automation, scalability, ambiguity, and uncertainty [12], [37], [47], [53], [59]. Software initiatives might come with several risks, assumptions, and requirements that are incompatible.…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Literature Review of AI-Based Software Requirements Prioritization Techniques

Anwar,

Bashir

2023

IEEE Access

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Software requirements show what the customer desires his software to do. They are the first stepping stone towards a successful software development project. With the increasing complexity of the software due to its size and feature base, it is vital to prioritize the requirements for efficient utilization of development resources. To achieve this, industrial organizations are devising new strategies and improved solutions even with the help of artificial intelligence (AI) tool set. Existing requirements prioritization techniques are human-intensive and suffer from several limitations like overlapping outcomes, scalability problems, time consumption, inaccuracy, and so on. Some of the problems can be solved with the inclusion of artificial intelligence algorithms and strategies. For this several AI-based requirements prioritization techniques have been proposed by applying Genetic Algorithms, Fuzzy Logic, Ant Colony Optimization, and Machine Learning. Literature witnesses some good review studies and surveys on conventional prioritization techniques but there exists none for AI-based techniques that identify not only their strengths but also their weaknesses, advantages of machine learning techniques over other AI-based requirements prioritization techniques, and limitations of applying AI-based techniques in requirements prioritization. This study presents a systematic literature review (SLR) of AI-based requirements prioritization approaches covering 46 papers published from 2000 to 2021. We have given this literature review a new dimension by conducting a parametric analysis of AI-based requirements prioritization techniques and we have identified these parameters after a thorough literature study. Some of the chosen parameters are generic (related to the prioritization process) and some are specific (related to AI techniques). This study has greatly helped us draw a clear line among AI-based techniques to show their domain of application to gain maximum advantage. Our findings will assist researchers, requirement analysts, and other stakeholders in making a wise decision to select the best requirements prioritization technique to gain optimal results.

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Section: ) Dabbagh and Lee (2015)mentioning

confidence: 99%