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

[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.     

Revisiting the supplier selection problem: An integrated approach for group decision support

Decision support for supplier selection is a highly researched theme in procurement management literature. However applications of group decision support theories are yet to be explored extensively in this domain. This study proposes an approach for group decision support for the supplier selection problem by integrating fuzzy Analytic Hierarchy Process (AHP) for group decision making and fuzzy goal programming for discriminant analysis. In the first step, the fuzzy AHP theory with the Geometric Mean Method has been used to prioritize and aggregate the preferences of a group of decision makers. Then consensus has been developed between these aggregated priorities using the Ordinal Consensus Improvement Approach. Subsequently, the consensual priorities of this group of decision makers have been integrated with fuzzy goal programming theory for discriminant analysis to provide predictive decision support. Finally it has been shown through a case study how the integrated approach using fuzzy AHP for group decision making and fuzzy goal programming with soft constraints has been more effective as compared to an existing approach for group decision making using only AHP.     

Web质量模糊评测矩阵的一致性判定

利用模糊层次分析法(FAHP)评测Web资源质量时,模糊逻辑的引入使成对比较矩阵的一致性判别成为难点。为此,设计最大隶属度元素值替代法,分析并改进基于最小二乘法的一致性判定法。依据判别结果对不一致的模糊矩阵进行修正,直到所有模糊矩阵都满足一致性阈值。分析结果表明,2种方法均能保证Web资源质量评测过程的合理性和结果的可靠性,且后者适用性更高。     

Integrating fuzzy theory and hierarchy concepts to evaluate software quality

This study proposes a software quality evaluation model and its computing algorithm. Existing software quality evaluation models examine multiple characteristics and are characterized by factorial fuzziness. The relevant criteria of this model are derived from the international norm ISO. The main objective of this paper is to propose a novel Analytic Hierarchy Process (AHP) approach for addressing uncertainty and imprecision in service evaluation during pre-negotiation stages, where comparative judgments of decision makers are represented as fuzzy triangular numbers. A new fuzzy prioritization method, which derives crisp priorities from consistent and inconsistent fuzzy comparison matrices, is proposed. The Fuzzy Analytic Hierarchy Process (FAHP)-based decision-making method can provide decision makers or buyers with a valuable guideline for evaluating software quality. Importantly, the proposed model can aids users and developers in assessing software quality, making it highly applicable for academic and commercial purposes.

A balanced solution of a fuzzy soft set based decision making problem in medical science

The purpose of this paper is two folded. Firstly, the concept of mean potentiality approach (MPA) has been developed and an algorithm based on this new approach has been proposed to get a balanced solution of a fuzzy soft set based decision making problem. Secondly, a parameter reduction procedure based on relational algebra with the help of the balanced algorithm of mean potentiality approach has been used to reduce the choice parameter set in the parlance of fuzzy soft set theory and it is justified to the problems of diagnosis of a disease from the myriad of symptoms from medical science. Moreover the feasibility of this proposed method is demonstrated by comparing with Analytical Hierarchy Process (AHP), Naive Bayes classification method and Feng's method.     

Development of a fuzzy ANP based SWOT analysis for the airline industry in Turkey

Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis has been widely used to evaluate alternative strategies in order to determine the best one for given business setting. This study aims at providing a quantitative basis to analytically determine the ranking of the factors in SWOT analysis via a conventional multi-criteria decision making method, Analytic Network Process (ANP). The ANP method is preferred in this study because of its capability to model potential dependencies among the SWOT factors. The study presents uniqueness in the way it incorporates inherent vagueness and uncertainty of the human decision making process by means of the fuzzy logic. The proposed SWOT fuzzy ANP methodology was implemented and tested for the Turkish airline industry. The results showed that the SWOT fuzzy ANP is a viable and highly capable methodology that provides invaluable insights for strategic management decisions in the Turkish airline industry, and can also be used as an effective tool for other complex decision making processes.     

A knowledge-based system for maintenance planning of highway concrete bridges

This paper presents a knowledge-based system that is used for maintenance planning of highway concrete bridges. The system includes functions for maintenance priority setting among bridges, feasible treatment assessment in each case, and maintenance planning for a bridge stock. Maintenance priorities are set using a scoring model with decision parameters appropriately weighted. Feasible treatments are determined based on bridge condition and other factors that accelerate deterioration. Decisions for maintenance planning result from a linear programming model and are based on priority ranking, cost and effectiveness characteristics of feasible treatments, and existing budget constraints. The system has been successfully evaluated with actual and simulated data.     

Microcomputer selection process for organizational information management

This paper presents a selection model based on the Analytic Hierarchy Process (AHP). The methodology uses relevant organizational needs, required operational support categories, and respective attributes of the proposed systems in a selection hierarchy. Deriving a priority structure associated with this hierarchy permits the systematic comparison of candidate systems, and thereby selecting the one that best suits the organization. The application of this model to the selection of an accounting information system is described. The methodology outlined in this paper has been used in several large organizations (insurance and industrial corporations) for selection of their “standard micro” (or mini) computer systems. These systems were installed for various decentralized applications.     

层次分析法在AUV智能决策中的应用研究

在海洋勘查使命中,AUV为了适应各种变化以完成使命必须进行智能决策并从多条恢复路径中选出最优者。为了寻求获得最优恢复路径的方法,分析了AUV智能决策的主要约束,建立了智能决策模型,基于层次分析法对AUV进行智能决策选取最优恢复路径的过程进行了定性的分析。仿真试验表明：AUV基于层次分析法所获得的最优恢复路径和专家的决策相一致,层次分析法能够有效解决多约束条件下AUV的智能决策问题。     

Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic

With regard to limits in budget, inevitably, one must choose prioritized projects in pavements rehabilitation and maintenance process. This paper looks into prioritization based upon a model including all effects of important factors like pavement condition index, traffic volume, road width as well as rehabilitation and maintenance cost. Since defining a model that introduces all those factors was difficult, a more advanced modeling named fuzzy logic was referred for the problem of prioritization. Although analytical hierarchy process can be used for decision making process as well, fuzzy modeling lets one have more precise choices for the outcome. Finally with the help of MATLAB software and coded M-files, inference engines such as Product engine, Dienes–Rescher and Lukasiewicz were all tested and the logical favorite separation for this application was found in product inference engine. As a case study some streets located in district No. 6 of Tehran municipality were selected and the favorite mathematical model was executed on those streets. This model was used for prioritizing these 131 sections.     

A systematic methodology to deal with the dynamics of customer needs in Quality Function Deployment

In the context of a customer-driven product or service design process, a timely update of customer needs information may not only serve as a useful indicator to observe how things change over time, but it also provides the company a better ground to formulate strategies to meet the future needs of its customer. This paper proposes a systematic methodology to deal with customer needs’ dynamics, in terms of their relative weights, in the QFD. Compared to previous research, its contribution is three-fold. First, it proposes the use of a forecasting technique which is effective to model the dynamics of Analytic Hierarchy Process (AHP) based importance rating. This is owing to the fact that the AHP has been applied very extensively in the QFD and there is, unfortunately, almost no tool to model the dynamics. Second, it describes more comprehensively on how future uncertainty in the weights of customer needs may be estimated and transmitted to the design attributes. Third, it proposes the use a quantitative approach that takes into account the decision maker’s attitude towards risk to optimize the QFD decision making analysis. Finally, an example based on a real-world application of QFD is provided to show the practical applicability of the proposed methodology.     

Fuzzy performance measurement of a supply chain in manufacturing companies

This paper is aimed to present a fuzzy decision making approach to deal with the performance measurement in supply chain systems. In the manufacturing environment, performance measurement is based on different quantitative and qualitative factors. Some of these factors may have a larger effect on the performance measure than others. Units of measure of the quantitative factors are different such as time, money, percentage, ratio, and counts. Thus, this paper presents a performance measurement approach based on fuzzy set theory and the pair-wise comparison of Analytical Hierarchy Process (AHP), which ensures the consistency of the designer’s assignments of importance of one factor over another to find the weight of each of the manufacturing activity in the departmental organization. In the proposed model, various input factors have been selected, and treated as a linear membership function of fuzzy type. It is tested on a numerical example. The approach provides an effective decision tool for the performance measurement of a supply chain in manufacturing environment.     

A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral

In this paper, a hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral is described. Interval type-2 fuzzy inference systems are used to perform edge detection and to calculate fuzzy densities for the decision process. A type-2 fuzzy system is used for edge detection, which is a pre-processing applied to the training data for better use in the neural networks. Another type-2 fuzzy system calculates the fuzzy densities necessary for the Sugeno integral, which is used to integrate results of the neural network modules. In this case, fuzzy logic is shown to be a good methodology to improve the results of a neural system facilitating the representation of the human perception. A comparative study is also made to verify that the proposed approach is better than existing approaches and improves the performance over type-1 fuzzy logic.     

Using fuzzy decision making system to improve quality-based investment

In this paper, fuzzy set theory is used to select the quality-based investment in small firm. Here a new algorithm, which will consider both exogenous and endogenous variables as factors, is proposed to formulate the problem. The structure of the algorithm is based on fuzzy decision-making system (FDMS), which uses fuzzy control rules. Hence, one exogenous factor and five endogenous factors mentioned above are determined as input variables and fuzzified using membership function concept. Then, the weights of these factors are fuzzified to ensure the consistency of the decision maker when assigning the importance of one factor over another. Applying IF-THEN decision rules, quality-based investments are scored. Also the comparison with Analytical Hierarchy Process (AHP) and Fuzzy Linguistic Approach (FLA) in respect to these scores is presented.     

Methods for fuzzy complementary preference relations based on multiplicative consistency

To improve the consistency of a preference relation is a hot topic in decision making. Wang and Chen (2008) gave a simple method to construct the complete fuzzy complementary preference relation from only n − 1 pairwise comparisons. However, some values may not be in the defined scope and need to be transformed, and thus some original information may be lost in the transformation process. In this paper, we propose a new method to avoid this issue based on the multiplicative consistency of the fuzzy complementary preference relation and apply it to fuzzy Analytic Hierarchy Process (AHP). An example is further given to illustrate our method.     

Effects of increasing fuzziness on analytic hierarchy process for spatial multicriteria decision analysis

Multicriteria decision analysis (MCDA) involves techniques which relatively recently have received great increase in interest for their capabilities of solving spatial decision problems. One of the most frequently used techniques of MCDA is Analytic Hierarchy Process (AHP). In the AHP, decision-makers make pairwise comparisons between different criteria to obtain values of their relative importance. The AHP initially only dealt with crisp numbers or exact values in the pairwise comparisons, but later it has been modified and adapted to also consider fuzzy values. It is necessary to empirically validate the ability of the fuzzified AHP for solving spatial problems. Further, the effects of different levels of fuzzification on the method have to be studied. In the context of a hypothetical GIS-based decision-making problem of locating a dam in Costa Rica using real-world data, this paper illustrates and compares the effects of increasing levels of uncertainty exemplified through different levels of fuzzification of the AHP. Practical comparison of the methods in this work, in accordance with the theoretical research, revealed that by increasing the level of uncertainty or fuzziness in the fuzzy AHP, differences between results of the conventional and fuzzy AHPs become more significant. These differences in the results of the methods may affect the final decisions in decision-making processes. This study concludes that the AHP is sensitive to the level of fuzzification and decision-makers should be aware of this sensitivity while using the fuzzy AHP. Furthermore, the methodology described may serve as a guideline on how to perform a sensitivity analysis in spatial MCDA. Depending on the character of criteria weights, i.e. the degree of fuzzification, and its impact on the results of a selected decision rule (e.g. AHP), the results from a fuzzy analysis may be used to produce sensitivity estimates for crisp AHP MCDA methods.     

Sensitivity analysis method to address user disparities in the analytic hierarchy process

Decision makers often face complex problems, which can seldom be addressed well without the use of structured analytical models. Mathematical models have been developed to streamline and facilitate decision making activities, and among these, the Analytic Hierarchy Process (AHP) constitutes one of the most utilized multi-criteria decision analysis methods. While AHP has been thoroughly researched and applied, the method still shows limitations in terms of addressing user profile disparities. A novel sensitivity analysis method based on local partial derivatives is presented here to address these limitations. This new methodology informs AHP users of which pairwise comparisons most impact the derived weights and the ranking of alternatives. The method can also be applied to decision processes that require the aggregation of results obtained by several users, as it highlights which individuals most critically impact the aggregated group results while also enabling to focus on inputs that drive the final ordering of alternatives. An aerospace design and engineering example that requires group decision making is presented to demonstrate and validate the proposed methodology.