Software Requirements Prioritization Research Articles

Choosing a Suitable Requirement Prioritization Method: A Survey

Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and budget. Many factors affect requirement prioritization such as stakeholder expectations, complexity, dependency, scalability, risk and cost. Therefore, finding the proper order of requirements is a challenging process. Hence, different types of requirements prioritization techniques have been developed to support this task. In this survey we propose a novel classification that can classify the prioritization techniques under two major classes: relative and exact prioritization techniques class where each class is divided into two subclasses. We also provide an overview about fifteen different requirements prioritization techniques that are classified under our proposed classification. Moreover, we make a comparison between methods that are related to the same subclass to analyze their strengths and weakness. Based on the comparison results, the properties for each proposed subclass of techniques are identified. Depending on these properties, we present some recommendations to help project managers in the process of selection the most suitable technique to prioritize requirements based on their project characteristics.

An Approach Based on Intuitionistic Fuzzy Sets for Considering Stakeholders’ Satisfaction, Dissatisfaction, and Hesitation in Software Features Prioritization

This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders’ satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders’ evaluations, between the satisfaction from offering certain features and the dissatisfaction from not offering the same features. Even with systematic, ordinal scale-based prioritization techniques, involved stakeholders may exhibit hesitation and uncertainty in their assessments. Our approach aims to address these challenges by employing the Binary Search Tree prioritization method and leveraging the mathematical framework of Intuitionistic Fuzzy Sets to quantify the uncertainty of stakeholders when expressing assessments on the value of software features. Stakeholders’ rankings, considering satisfaction and dissatisfaction as features prioritization criteria, are mapped into Intuitionistic Fuzzy Numbers, and objective weights are automatically computed. Rankings associated with less hesitation are considered more valuable to determine the final features’ priorities than those rankings with more hesitation, reflecting lower indeterminacy or lack of knowledge from stakeholders. We validate our proposed approach with a case study, illustrating its application, and conduct a comparative analysis with existing software requirements prioritization methods.

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems.

In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.

Literatur Review: Penerapan Metode Analytic Hierarchy Process Sebagai Teknik Prioritas Kebutuhan Perangkat Lunak

AHP is one of the popular techniques used in prioritizing software requirements. This study aims to determine the application of AHP as a software requirements priority technique by reviewing thirty literature related to the application of AHP as a software requirements prioritization technique pub-lished in 2017 to 2023. The results of the study explain that the application of AHP has several ad-vantages, but on the other hand AHP also has some drawbacks. Over time, a number of literatures have discussed the development of AHP which aims to minimize weaknesses in the application of AHP. The development of AHP is by integrating AHP with Planning Game, MoSCoW, QFD, and Fuzzy. In addition, development is also carried out by adding new algorithms, namely ReDCCahp and Enhanced AHP. The results of this study provide a useful reference for researchers and practitioners in the field of requirements engineering in prioritizing software requirements using AHP.

Exploring Software Requirements Prioritization techniques: Types, Evolution, and Metrics for effective Decision-Making

Exploring Software Requirements Prioritization techniques: Types, Evolution, and Metrics for effective Decision-Making

The use of knapsack 0/1 in prioritizing software requirements and Markov chain to predict software success

The use of knapsack 0/1 in prioritizing software requirements and Markov chain to predict software success

Exploratory study of existing research on software requirements prioritization: A systematic literature review

AbstractRequirements prioritization is giving priority to software requirements on the basis of its importance. The current research paper addresses the importance and limitations of existing research on requirements prioritization. A lot of requirements prioritization techniques are presented by authors, but these techniques have certain limitations. Not all techniques are either applied or suitable to prioritize all types of requirements. Similarly, in these techniques, only few are scalable for large size requirements. The objective of the current research is to identify all possible software requirements prioritization techniques and to show the suitability of these techniques to prioritize different types and sizes of requirements. To achieve research objectives, a systematic literature review (SLR) is carried out. In conducting SLR, planned protocol is designed, which include all the necessary steps necessary for SLR process. As a result of SLR, 60 primary studies were selected for data extraction as a result of SLR. Outcome of SLR shows that not all techniques are scalable to prioritize any size requirements. Also, there are few techniques found that are either applied on all types of requirements or not suitable to prioritize all types of requirements as these techniques are specifically designed for particular types of requirements.

Prioritizing Use Cases: A Systematic Literature Review

The prioritization of software requirements is necessary for successful software development. A use case is a useful approach to represent and prioritize user-centric requirements. Use-case-based prioritization is used to rank use cases to attain a business value based on identified criteria. The research community has started engaging use case modeling for emerging technologies such as the IoT, mobile development, and big data. A systematic literature review was conducted to understand the approaches reported in the last two decades. For each of the 40 identified approaches, a review is presented with respect to consideration of scenarios, the extent of formality, and the size of requirements. Only 32.5% of the reviewed studies considered scenario-based approaches, and the majority of reported approaches were semiformally developed (53.8%). The reported result opens prospects for the development of new approaches to fill a gap regarding the inclusive of strategic goals and respective business processes that support scenario representation. This study reveals that existing approaches fail to consider necessary criteria such as risks, goals, and some quality-related requirements. The findings reported herein are useful for researchers and practitioners aiming to improve current prioritization practices using the use case approach.

Improving Software Requirements Prioritization through the Lens of Constraint Satisfaction

Requirements prioritization is a critical activity during the early software development process, which produces a set of key requirements to implement. The prioritization process offers a parity among the requirements based on multiple characteristics, including end-users’ preferences, cost to implement, and technical dependencies. This paper presents an interactive method to requirements prioritization that leverages the pairwise comparisons and a constraint solver. Our method employs an interactive accumulation of knowledge from the requirements analyst when the relative priority among the requirements cannot be determined based on the existing knowledge from the requirements documents. The final ranking of the requirements is produced via the constraint solver and interactive pairwise comparisons. We evaluate the proposed method using the requirements from a real healthcare project. The proposed prioritization method relying on a constraint solver outperforms state-of-the-art interactive prioritization methods in terms of effectiveness and robustness to analyst’s errors.

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.

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

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

PUGH Decision Trapezoidal Fuzzy and Gradient Reinforce Deep Learning for Large Scale Requirement Prioritization

Objective: To prioritize requirements for large scale software projects within time involving uncertainty in the opinions among different stakeholders. Methods: We propose Pugh Trapezoidal Fuzzy and Gradient Reinforce Learning (PTF-GRL) methods for large scale software requirement prioritization. A Pugh Decision-based Trapezoidal Fuzzy Requirement Selection model is designed, inputting the functional and non-functional requirements of the corresponding stakeholders. With the assistance of Trapezoidal Fuzzy Inference, the qualitative factors are mapped with the corresponding numeric factors, which increases the computational efficiency. Findings: Performance is analyzed based on four parameters: The first parameter is accuracy and our method showed improvement of 4%, 7% and 3% compared to JRD-SCRUM, IFS and SRPTackle respectively. The second parameter is prioritization time and found that our method had reduced time of 30%, 37% and 39% compared with existing methods. The third parameter is precision and it was found that our method improves precision by 6%, 10% and 5% compared with the other two methods. The final parameter we consider is the test suite execution and our method showed improvement of 12%, 19% and 5% compared with the existing two methods. Novelty/Applications: The originality of this work indicates the better performance along with the optimal test suite execution even considering the uncertainty factor in the proposed method compared with existing similar methods. Keywords: Software Project; Pugh Decision Matrix; Trapezoidal Fuzzy Inference; Gradient Orientation; Reinforce Learning; Requirement Prioritization

Enhancing Reusability: An Integrated Framework for Software Requirements Classification and Prioritization

Requirements Prioritization (RP) is the process of planning, identifying, rating and organizing requirements to avoid breach of trust, contract or agreement. The need of RP is inevitable if the concerns, cost and budget estimation of the stakeholders are considered as primary parameters in a software project. In order to ensure the reusability of existing modules, the new requirements must be classified and coordinated with the current ones in the repository, which guarantees the monetary use of assets. In this research, we have suggested a new framework for software requirements classification and prioritization. This framework is called the Software Requirements Classification and Prioritization Framework (SCPF). It will help the software project managers in managing the requirements at the initial stage of software development. We have identified the key factors and developed a taxonomy in order to classify these factors. This study likewise devised a new RP algorithm, which is based on the normalized aggregation values of the identified factors, and also contributes for the requirements classification by using [Formula: see text]-Nearest-Neighbor (KNN) classifier based on the well-known Normalized Compression Distance (NCD) similarity measure. In addition to this, we have implemented the SCPF by developing a Web-based application which provides the computation features for requirements classification and prioritization. Furthermore, we have conducted a case study and an industry survey to evaluate the results of the SCPF. A comparative study is also presented to show the effectiveness of SCPF with respect to three different state-of-the-art RP approaches. The computed SCPF results are very promising and showed significant improvement in the quality of requirements classification and prioritization.

Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design

Software Requirements Prioritization (SRP) is one of the crucial processes in software requirements engineering. It presents a challenging task to decide among the pool of requirements and the variance of the stakeholder’s needs in prioritizing requirements. Semi-automated requirements prioritization is implemented in both manual and automatic processes. When prioritizing requirements, these aspects such as importance, time, cost and risk, should be taken into account. The emergence of machine learning is advancing to improve and automate the SRP process whereby decision making can be performed with minimal human intervention. Incorporating machine learning approaches in prioritization techniques can be implemented in the ranking process and classifying the priority group of the software requirements. A Semi-Automated Requirements Prioritization framework (SARiP), which implements semi-automatic process in requirements prioritization is proposed. SARiP concentrates on the areas related to prediction of requirements priority group and ranks requirements using classification tree and ranking algorithm. SARiP has been successfully evaluated in the government sector domain by the i-Tegur team from the Department of Information Technology, Ministry of Housing and Local Government of Malaysia (KPKT). 80% of the participants agreed that SARiP is extremely likely to help the participants in prioritizing the requirements for their projects. All participants agreed that SARiP is reliable and useful. Recording the requirements and results for the prioritization will be considered for future work and traceability function will be included to trace the requirements changes.

MCBRank Method to Improve Software Requirements Prioritization

Software Requirements Prioritization is an important issue that has a more profound effect on the overall quality of software development. Application of software requirements prioritization provides benefits to minimize risks in software development so that the most important and most impactful requirements are given priority. This paper presents a proposed software requirements prioritization method named MCBRank, which incorporates renowned MoSCoW Method and Case-Based Ranking to improve prioritization correctness. It elaborates on the implementation of MCBRank in an empirical investigation to determine software requirements prioritization for a potential e-library system. The investigation allows the software requirements prioritization process to be implemented by using the proposed MCBRank method. A role-playing empirical investigation with 30 respondents prioritized 31 software requirements, and the results were measured by Cohen Kappa. The kappa results show that MCBRank achieves a better agreement towards the Gold Standard with kappa value of 0.60. Therefore, the investigation results support that MCBRank improves the importance of ranking correctness, representing the stakeholders’ wants and the organization's actual needs for the potential e-library system.

Using Social Network and Fuzzy Set Theory for Elicitation and Prioritization of Software Requirements

Using Social Network and Fuzzy Set Theory for Elicitation and Prioritization of Software Requirements

OurRank: A Software Requirements Prioritization Method Based on Qualitative Assessment and Cost-Benefit Prediction

Requirements prioritization is an activity aimed at determining the essential requirements to include in a software release. Although there are several prioritization methods to systematize this task, there are still unresolved challenges. Existing methods do not guarantee that requirements prioritization meets stakeholder expectations and goals. This is because most prioritization methods operate by considering only quantitative information, making it difficult to formally capture stakeholder interests and perspectives that can rather be made explicit in qualitative terms. Likewise, methods including qualitative information only consider elements associated with benefit estimation, that is, positive aspects of the project, but neglect costs or negative aspects. As a result, the prioritization process is driven by a partial view of constraints. Such methods also fail at capturing and combining expert knowledge that decision-makers can bring into the decision-making process. In this research, we propose a novel method for software requirements prioritization, which facilitates incorporating experts’ qualitative assessment at the outset of the prioritization process and considers both benefit and cost constraints. Details of the method are presented, together with a case study describing a real application scenario. Recommendations and guidelines regarding the application of the method are proposed based on the results of the case study.

E-AHP: An Enhanced Analytical Hierarchy Process Algorithm for Priotrizing Large Software Requirements Numbers

One of the main activities of software requirements analysis is requirements prioritization. The wrong requirements prioritization is risky as it leads to many software failures. The current requirements prioritization techniques can’t deal with large requirement numbers efficiently, which is considered one of their main issues. Many researchers have agreed that the analytical hierarchy process (AHP) is one of the best prioritization techniques as it produces highly accurate results. AHP has two main problems: scalability and inconsistency. These problems have motivated us to propose an improved version of AHP for software requirements prioritization, namely Enhanced AHP (E-AHP). A performance evaluation has been done for the conventional AHP, E-AHP, and one of the recent algorithms that also try to solve the AHP scalability problems, namely removing eigenvalues and introducing the dynamic consistency checking algorithm into AHP (ReDCCahp) algorithms The evaluation shows which algorithm takes the least time, uses the least memory, produces the most consistent and accurate results, and has the highest scalability. The three algorithms have been evaluated by running their codes using different numbers of requirements ranging from 10 to 500. The results show that E-AHP is more scalable, takes the least time, uses the least memory, and produces the most consistent and accurate results compared to the other two algorithms. That becomes remarkable when the number of requirements increases. Therefore, E-AHP is suitable to be applied in large software projects, as it can deal efficiently with the large software requirements numbers.

Measures Driven Change Impact Analysis Approach for Prioritizing Software Requirements Changes

Measures Driven Change Impact Analysis Approach for Prioritizing Software Requirements Changes

Use of Natural Language Processing in Software Requirements Prioritization – A Systematic Literature Review

Purpose: Research involves the creation and implementation of new ideas by keeping the existing work as a foundation. The literature review done in this paper is to familiarise and to know about the domain of research to integrate the existing ideas with the new ones. Methodology: The literature that is required for this study is chosen from multiple secondary sources such as journals, conference proceedings, and web resources. All the pieces of literature are carefully studied and summarised. This is further used to arrive at Research agendas and Research gaps. Findings/Result: It has been observed and understood that Natural Language Processing (NLP) is a field involving analysis and processing of textual contents. It also requires Machine Learning Algorithms to support the processing. This combination has already been used in various domains, the important one being the health sector. EMR data is huge and NLP can successfully process and prioritize them in different dimensions. In that direction, the same concept and technology can be applied to Software Engineering also and Requirements can be prioritized. Originality: This literature review study is carried out using secondary data which is collected through various online sources. The information thus gathered will be used in the future to build upon existing theory and framework or build a new methodology. It is also seen that any conclusion or decision is not biased or unidirectional. A sincere effort is made to identify a research topic to carry out the research. Paper Type: Literature Review.