Software Requirements Selection Research Articles

Generating large dataset for software requirements prioritization and selection under fuzzy environment

Requirements elicitation is a key sub-process of requirements engineering in which various types of software requirements such as functional and non-functional requirements, testing requirements etc., are identified according to the needs of the stakeholders. After the completion of the requirements elicitation process, there may be a large set of requirements and practically it is not possible to implement all requirements due to budget, time and other constraints of an organization. Under this situation, software developers select the highest ranked software requirements from the list of the elicited software requirements for different releases of software so that a successful software product can be developed. The selection of software requirements for different releases is a difficult task because it requires the participation of various stakeholders. In the field of science and engineering, datasets play an important role during experimental work. Based on our review, we found that in the literature of software engineering less attention is given on the generation of the datasets particularly in the domain of software requirements prioritization and selection (SRPS). Therefore, to address this issue, in this paper we present a four-step method for generating the dataset for SRPS research, i.e. (a) identification of stakeholders, (b) elicitation of software requirements using the goal-oriented concept, (c) formation of decision maker committee and (d) evaluation of the functional requirements and non-functional requirements by decision makers under fuzzy environment. The proposed method has been applied to generate the dataset for the requirements of an institute examination system.

An Integrated Method for the Selection of Software Requirements Using Fuzzy TOPSIS and Genetic Algorithm

An Integrated Method for the Selection of Software Requirements Using Fuzzy TOPSIS and Genetic Algorithm

Software requirement selection using a combined multi‐objective optimisation technique

The optimal requirements selection set aims primarily at careful search for the best requirements set of the next release of software during development process. This procedure is widely defined as the next release problem (NRP), which is also classified as NP-hard dilemma. Several techniques, in literature, have been proposed to tackle NRP. However, in real examples, the earlier studies still immature as NRP still suffers interactions and restrictions that makes the problem more complicated. Although few interesting works have been presented, yet NRP, based on our study, could be further investigated and effectively tackled. In this research, therefore, NRP is devised as a multi-objective optimisation problem. Two clashing objectives (satisfaction and cost) and two constraints (interactions forms) are formulated. To tackle NRP effectively, a new hybrid genetic and artificial bee colony algorithm (HGABC) is introduced. HGABC combines features of genetic and artificial bee colony algorithms. Experimental study, using case studies and three criteria, have been conducted to show HGABC's power of generating non-dominated effective Pareto solutions versus the state-of-the-art algorithms. Results indicate that HGABC does not just outperform its rivals, yet also gives better Pareto solutions in terms of diversity and quality for almost all the instances of this problem.

A comparison between fuzzy AHP and fuzzy TOPSIS methods to software requirements selection

The fuzzy set theory as one of the key agents of artificial intelligence has been used to deal with vagueness and imprecision during the decision-making process. The software requirements selection is a multicriteria decision making problem which has a key importance for several software development companies. Few methods have been developed to select the software requirements from the list of the elicited requirements using fuzzy analytic hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods. Based on our review, we found that little attention is given on the comparison between the fuzzy AHP and fuzzy TOPSIS methods in the context of the software requirements selection problem. To address this issue, this paper presents a comparative analysis of fuzzy AHP and fuzzy TOPSIS methods by considering the small and large set of requirements of an institute examination system based on the following factors: agreement measure, time complexity, rank reversal issue, and number of judgments by decision makers. Based on the comparative study, we found that both fuzzy AHP and fuzzy TOPSIS methods produce the same set of functional requirements based on agreement measure metric in both dataset-1 and dataset-2. Fuzzy AHP requires less time to generate the ranking order in dataset-1; and fuzzy TOPSIS performs better then fuzzy AHP in dataset-2. Fuzzy AHP causes the rank reversal issue; and there is no rank reversal issue in fuzzy TOPSIS and it produces the consistent results. Fuzzy TOPSIS requires less judgment by decision makers then fuzzy AHP. Finally, we discuss the future research directions in the area of SRs selection problem.

Fuzzy logic driven security requirements engineering process

Security requirements are non-functional requirements which are employed to protect the information from unauthorized users. After the requirements elicitation process, a system may have several requirements; and practically it is not possible to provide security for all the requirements because of the budget and other constraints of an organization. So, it is important to identify the selected set of software requirements. Selection of software requirements based on different criteria like cost and security is a multi-criteria decision making (MCDM) problem in which different stakeholders are involved and they specify their preferences on software requirements using linguistic variables. Based on our review, we found that existing methods like multilateral, problem frame, goal oriented, and common criteria do not support MCDM methods for the identification of those requirements which need more security during the development process. Therefore, to address this issue, in this paper, we present a fuzzy logic driven security requirements engineering process. Finally, the proposed methodology is explained with the help of an example.

Prioritization and Selection of the Software Requirements using Rough-Set Theory

Software requirements prioritization and selection is an important activity of the requirements elicitation process. The objective of this process is to prioritize the software requirements so that high-ranked requirements can be selected for implementation. The information stored in the fuzzy-based methods contains vague or uncertain statements of stakeholder’s perceptions. This is modeled by the different types of membership functions. In these methods, selection of the membership function depends on the subjective judgements and thus it lacks objectivity. As a result, it may change the ranking values of the software requirements. To address this issue, this paper presents a method for the prioritization and selection of the software requirements using rough-set theory. The proposed method is compared with three fuzzy-based methods which are used to calculate the ranking values of software requirements. Both small and large set of requirements of an examination system have been used in comparative study based on the following criteria: (a) agreement measure, (b) number of judgements by decision makers, and (c) modeling of uncertainty.

Partial Selection of Software Requirements: A Fuzzy Method

Partial Selection of Software Requirements: A Fuzzy Method

Software Requirements Selection with Incomplete Linguistic Preference Relations

Software requirements (SRs) selection is a multicriteria group decision making (MCGDM) problem whose objective is to select the SRs from the pool of the requirements on the basis of different criteria. In MCGDM, different decision makers have different opinions of the same requirement so it is difficult to decide which set of SRs to implement during the different releases of the software. During the MCGDM process, decision makers may use linguistic variables to specify preferences of requirements over other requirements. In real life applications, it has been observed that sometimes decision makers cannot evaluate the SRs due to their lack of knowledge and limited expertise related to the problem domain. In this situation, incomplete linguistic preference relations (LPRs) are constructed. In literature, SRs selection with incomplete LPRs is still an unresearched problem. Therefore, to address this issue, a method is presented for the selection of SRs with incomplete LPRs. Finally, the applicability of the proposed method is explained with the help of an example.

Selection of software requirements using TOPSIS under fuzzy environment

The goal of the decision making process is to find out the most appropriate alternative(s) from the set of the alternatives based on different criteria. Software requirements selection is a multi-criteria decision making (MCDM) problem whose objective is to choose the software requirements (SRs) from the set of the SRs so that the need of the stakeholders can be implemented during the development of software. Due to the increasing complexity of the socio-economic environment, multiple decision makers are involved during SRs selection process because it is difficult for single decision maker to deal with all the SRs. During the SRs selection process, decision makers use linguistic terminologies to specify the preferences of the SRs. Therefore, this paper presents a decision making method for the selection of the SRs using fuzzy TOPSIS method in which linear scale transformation is used to construct the fuzzy decision matrix based on the triangular fuzzy numbers. Finally, the applicability of the proposed method is demonstrated with the help of an example.

Dependency-aware software requirements selection using fuzzy graphs and integer programming

One of the critical activities in software development is Requirements Selection, which is to find an optimal subset of the software requirements (features) with the highest value for a given budget. The values of the requirements, however, may depend on one another. Such Value Dependencies have not been considered by the existing requirements selection methods, leading to user dissatisfaction and loss of value and reputation in software projects. To mitigate this, we propose Dependency-Aware Requirements Selection (DARS) as an expert system, which explicitly accounts for value dependencies in software projects. At the heart of DARS is an Integer Linear Programming (ILP) model that reduces the risk of value loss by considering value dependencies among the requirements. These value dependencies are identified from the preferences of the users for the requirements. The validly of DARS is verified by studying a real-world software project as well as carrying out simulations. Our results demonstrate a significant reduction in value loss when DARS is employed. Also, the ILP model of DARS proved scalable to large requirement sets (experimented for up to 3000). The results of our study can be extrapolated to a wide range of expert systems that concern selecting value-dependent items.

A risk-driven multi-objective evolutionary approach for selecting software requirements

Exploring iterative and incremental approaches, current software projects define various delivery releases, which ought to be developed within budgetary constraints, but raising customers’ satisfaction as much as possible. In search based software engineering, such a problem is referred to as next release problem (NRP), in which the selection of software requirements for the next release is reformulated as an optimization problem. More recently, NRP proposals have evolved to a multi-objective perspective, providing non-dominated recommendations that balance the trade-off surface with respect to customers’ satisfaction and development cost. Despite key advancements in NRP proposals, most of them do not deal with software risks, which represent a vital aspect that can deeply impact on project cost and customers’ satisfaction. In such a direction, using multi-objective evolutionary algorithms (MOEAs), this paper proposes and evaluates a risk-driven systematic approach for the NRP problem, in which a risk analysis is incorporated for estimating the impact of software risks in development cost and customers’ satisfaction. By contrasting three different well-known MOEAs, empirical results based on two semi-real datasets reveal the potential efficiency and practical applicability, bringing not only more realistic, precise and meaningful estimations, but also finding a significant percentage of unanticipated solutions.

Selection and Prioritization of Software Requirements Applying Verbal Decision Analysis

During the software development process, the decision maker (DM) must master many variables inherent in this process. Software releases represent the order in which a set of requirements is implemented and delivered to the customer. Structuring and enumerating a set of releases with prioritized requirements represents a challenging task because the requirements contain their characteristics, such as technical precedence, the cost required for implementation, the importance that one or more customers add to the requirement, among other factors. To facilitate this work of selection and prioritization of releases, the decision maker may adopt some support tools. One field of study already known to solve this type of problem is the Search‐Based Software Engineering (SBSE) that uses metaheuristics as a means to find reasonable solutions taking into account a set of well‐defined objectives and constraints. In this paper, we seek to increase the possibilities of solving the Next Release Problem using the methods available in Verbal Decision Analysis (VDA). We generate a problem and submit it so that the VDA and SBSE methods try to resolve it. To validate this research, we compared the results obtained through VDA and compared with the SBSE results. We present and discuss the results in the respective sections.

Modeling and Selection of Interdependent Software Requirements Using Fuzzy Graphs

Software requirement selection is to find an optimal set of requirements that gives the highest value for a release of software while keeping the cost within the budget. However, value-related dependencies among software requirements may impact the value of an optimal set. Moreover, value-related dependencies can be of varying strengths. Hence, it is important to consider both the existence and the strengths of value-related dependencies during a requirement selection. The existing selection models however, either assume that software requirements are independent or they ignore strengths of requirement dependencies. This paper presents a cost-value optimization model that considers the impacts of value-related requirement dependencies on the value of selected requirements (optimal set). We have exploited algebraic structure of fuzzy graphs for modeling value-related requirement dependencies and their strengths. Validity and practicality of the work are verified through carrying out several simulations and studying a real world software project.

A Multi Objective Teacher-Learning-Artificial Bee Colony (MOTLABC) Optimization for Software Requirements Selection

Background/Objectives: To select optimal software requirements by introducing Multi-objective Teacher-Learning- Artificial Bee Colony Optimization. Methods/Statistical Analysis: Teaching learning based optimization for the multi-objective software requirements selection has two objectives of minimized cost and maximum client satisfaction. Similarly the constraints namely interaction constraints and cost threshold constraints are considered. However, the efficiency of the software product development can be improved further when more efficient optimization techniques is used for the selection of software requirements along with consideration of more objectives and more constraints in larger real datasets. Findings: In this article, a hybrid optimization technique named Multi-Objective Teacher-Learning Artificial Bee Colony Optimization (MOTLABC) is proposed with set of multiple objectives and constraints. The objectives are minimum cost, maximum client satisfaction, minimum time consumption and maximum reliability. The constraints such as time threshold constraint, interaction constraints and cost threshold constraints are considered. The hybrid approach of MOTLABC with the above objectives improves the collection of set of needs for the development of the software. The Pareto optimal problem occurs in multi objective optimization solutions is resolved by the use of Pareto tournament function. Improvements/Applications: The experimental consequences prove that they obtained results perform improved than algorithms proposed in the literature.

Comparing the performance of quantum-inspired evolutionary algorithms for the solution of software requirements selection problem

ContextIn requirements engineering phase of the software development life cycle, one of the main concerns of software engineers is to select a set of software requirements for implementation in the next release of the software from many requirements proposed by the customers, while balancing budget and customer satisfaction. ObjectiveTo analyse the efficacy of Quantum-inspired Elitist Multi-objective Evolutionary Algorithm (QEMEA), Quantum-inspired Multi-objective Differential Evolution Algorithm (QMDEA) and Multi-objective Quantum-inspired Hybrid Differential Evolution (MQHDE) in solving the software requirements selection problem. MethodThe paper reports on empirical evaluation of the performance of three quantum-inspired multi-objective evolutionary algorithms along with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The comparison includes the obtained Pareto fronts, the three performance metrics – Generational Distance, Spread and Hypervolume, attained boundary solutions, and size of the Pareto front. ResultsThe results reveal that MQHDE outperformed other methods in producing high quality solutions; while QMDEA is able to produce well distributed solutions with extreme boundary solutions. ConclusionThe hybridization of Differential Evolution with Genetic Algorithms coupled with quantum computing concepts (MQHDE) provided a means to effectively balance the two issues of multi-objective optimization - convergence and diversity.

On Fuzzy Qualitative and Quantitative Softgoal Interdependency Graph

Functional requirements serve as criteria for the selection and prioritization of software requirements. The objective of this paper is to propose an efficient method for the prioritization of non functional requirements. In this paper we use , inverse function arithmetic principle and graded mean integration for the elicitation of decision maker's weight for the qualitative and quantitative softgoal interdependency graph to model the non-functional requirements. Finally the utilization of proposed method is demonstrated with the help of an example. Keywordgoal Interdependency graph (SIG), Non-functional requirements (NFR), Trapezoidal fuzzy numbers (TFN), FQQSIG.

Teaching learning based optimization with Pareto tournament for the multiobjective software requirements selection

Software requirements selection is a problem which consists of choosing the set of new requirements which will be included in the next release of a software package. This NP-hard problem is an important issue involving several contradictory objectives which have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to satisfy the highest number of customer requirements. Furthermore, when managing real instances of the problem, the requirements tackled suffer interactions and other restrictions which make the problem even harder. In this paper, a novel multi-objective teaching learning based optimization (TLBO) algorithm has been successfully applied to several instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer׳s satisfaction. In addition, three interaction constraints have been also managed. In this context, the original TLBO algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out in order to prove the effectiveness of the multiobjective proposal. In fact, the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature.

Partial Selection of Agile Software Requirements

Prioritization And Selection (PAS) of requirements is an important concern of Agile software development. Existing PAS techniques are primarily binary selection of requirements and postponing lower-priority requirements to the future. As a result, lower-priority requirements may be constantly delayed until they never have a chance to be implemented at all. However, ignoring lower-priority requirements may threaten the quality of software. In this paper, we propose the concept of partial selection as an alternative to binary selection of Agile software requirements rather than ignoring them altogether or postponing them to the future releases. We further contribute a goal-oriented framework that allows requirements to be partially satisfied when tolerated. The framework specifies the fuzzy priority of requirements and accomplishes partial selection through relaxing the satisfaction level of requirements with respect to their priority in the release backlog of software. The experimental results show that partial selection of requirements will reduce the number of neglected requirements when compared to binary selection.

Differential evolution with Pareto tournament for the multi-objective next release problem

Software requirements selection is the engineering process in which the set of new requirements which will be included in the next release of a software product are chosen. This NP-hard problem is an important issue involving several contradictory objectives that have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to cover the highest number of customer requirements. Furthermore, in real instances of the problem, the requirements tackled suffer interactions and other restrictions which complicate the problem. In this paper, we use an adapted multi-objective version of the differential evolution (DE) evolutionary algorithm which has been successfully applied to several real instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer’s satisfaction, and with three interaction constraints. On the other hand, the original DE algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out to demonstrate the effectiveness of the multiobjective proposal and the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature under a set of public datasets.

Software requirements selection and prioritization using SBSE approaches: A systematic review and mapping of the literature

The selection and prioritization of software requirements represents an area of interest in Search-Based Software Engineering (SBSE) and its main focus is finding and selecting a set of requirements that may be part of a software release. This paper presents a systematic review and mapping that investigated, analyzed, categorized and classified the SBSE approaches that have been proposed to address software requirement selection and prioritization problems, reporting quantitative and qualitative assessment. Initially 39 papers returned from our search strategy in this area and they were analyzed by 18 previously established quality criteria. The results of this systematic review show which aspects of the requirements selection and prioritization problems were addressed by researchers, which approaches and search techniques are currently adopted to address these problems, as well as the strengths and weaknesses in this research area highlighted from the quality criteria.