Requirements Prioritization Research Articles

Security Requirements Prioritization Techniques: A Survey and Classification Framework

Security requirements Engineering (SRE) is an activity conducted during the early stage of the SDLC. SRE involves eliciting, analyzing, and documenting security requirements. Thorough SRE can help software engineers incorporate countermeasures against malicious attacks into the software’s source code itself. Even though all security requirements are considered relevant, implementing all security mechanisms that protect against every possible threat is not feasible. Security requirements must compete not only with time and budget, but also with the constraints they inflect on a software’s availability, features, and functionalities. Thus, the process of security requirements prioritization becomes an integral task in the discipline of risk-analysis and trade-off-analysis. A sound prioritization technique provides guidance for software engineers to make educated decisions on which security requirements are of topmost importance. Even though previous research has proposed various security requirement prioritization techniques, none of the existing research efforts have provided a detailed survey and comparative analysis of existing techniques. This paper uses a literature survey approach to first define security requirements engineering. Next, we identify the state-of-the-art techniques that can be adopted to impose a well-established prioritization criterion for security requirements. Our survey identified, summarized, and compared seven (7) security requirements prioritization approaches proposed in the literature.

A Review: Requirements Prioritization Criteria Within Collaboration Perspective

The attributes or criteria used in the requirements prioritization process become an essential reference in calculating priorities. Most of the techniques are used to increase the value impacting business success. On the contrary, there are limitations on cost, time, and resources for developing software. Therefore, the requirements prioritization process often requires collaboration from the perspectives involved. So far, the pattern and basis have not been seen in the criteria used in the requirements prioritization process. Consequently, there need to be other factors that become a reference so that the selection of criteria is appropriate. This study identifies criteria based on the categorized perspectives of requirements prioritization. A systematic literature review presents criteria for prioritizing requirements from multiple collaborative perspectives. Findings show that the criteria in requirements prioritization can be classified into beneficial and non-beneficial, where business value and development cost are the most frequently used criteria. Furthermore, the involvement of multiple perspectives in requirements prioritization focuses on the client’s and developer’s perspectives. The findings also reveal that some of the challenges in the requirements prioritization process are biases by stakeholders, reducing pairwise comparison, and scalability. In the future, it will be investigated whether the selection of criteria correlated with stakeholder perspectives will increase the accuracy of priorities. Thus, the contribution of this paper is to recommend criteria from stakeholders’ perspectives.

Improve the cervical cancer prevention behaviors through mobile-based educational intervention based on I-CHANGE model: study protocol for a randomized controlled trial

BackgroundApplications of mobile technologies (mHealth) have the potential to reduce health inequalities, give patients more control over their health, and improve health care’s cost-effectiveness. The widespread use of mobile phones offers us a new way to prevent cervical cancer. The objective of the study was to design and develop a mobile phone application (app) that aims to conduct a behavioral intervention for women and to evaluate the efficacy of the app-based intervention.MethodsThis study involves 5 phases. In the first phase, understanding women’s perspectives will be identified using a qualitative approach based on the I-Change model. In phase 2, the results from the qualitative approach and requirement prioritization through providing experts’ perspectives will be done. The main outputs of this phase will be resulted in prioritizing the main measurable effective variables of the I-Change model. Phase 3 will be processed for the development and psychometric of an assessment tool regarding selected constructs. In phase 4, the App framework and content development will be performed. In phase 5, a three-armed, parallel-design randomized controlled trial will be conducted on women. Two hundred ten women will be randomly assigned to three groups including two intervention groups and one control group. The intervention groups included the following: (1) a mobile application and (2) a digital book. The data will be evaluated using tools designed and constructed in phase 3 of the study at baseline in 3-month follow-up assessments. The impact of the two interventions on cervical cancer prevention behaviors through mobile-based educational intervention will then be evaluated.DiscussionA theory-based health education program using a mobile app to improve cervical cancer-preventive behaviors will be implemented for the first time in Iran. With an effective health mobile-based educational design, it is very important to determine whether Iranian women will be motivated to adhere to preventive behavior related to CC.Trial registrationIranian Clinical Trial Register IRCT20181205041861N3. Registered V2.0 on 26 October 2021.

Exploring Attractive Quality Requirements for Short Food Supply Chain Digital Platforms

Perceptions towards unsustainable supply chain practices in global, mainstream food systems are motivating a shift towards short food supply chains. Short food supply chains are developed to remove the physical and social distances between producer and consumer. Advances in digital technologies offer promise for short food supply chains, including platforms that can enable real-time data flow, create visibility, and support sustainable practices. This research aims to prioritise attractive quality requirements of short food supply chain digital platforms. The methodology consists of a literature review and Kano analysis for requirements prioritisation. The results show that the requirements span across the four Kano categories, attractive quality encompassing the largest number of requirements. The attractive quality requirements identified offer increased levels of satisfaction when present and have limited negative impact when missing. Therefore, they are considered exciting for potential users of a system. The limitations of the research and areas of future work are presented.

Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis.

Electronic commerce (E-commerce) through digital platforms relies on diverse user features to provide a better user experience. In particular, the user experience and connection between digital platforms are exploited through semantic emotions. This provides a personalized recommendation for different user categories across the E-commerce platforms. This manuscript introduces a Syntactic Data Inquiring Scheme (SDIS) to strengthen the semantic analysis. This scheme first identifies the emotional data based on user comments and repetition on the E-commerce platform. The identifiable and non-identifiable emotion data is classified using positive and repeated comments using the deep learning paradigm. This classification attunes the recommendation system for providing best-affordable user services through product selection, ease of access, promotions, etc. The proposed scheme strengthens the user relationship with the E-commerce platforms by improving the prioritization of user requirements. The user’s interest and recommendation factors are classified and trained for further promotions/recommendations in the learning process. The recommendation data classified from the learning process is used to train and improve the user-platform relationship. The proposed scheme’s performance is analyzed through appropriate experimental considerations. From the experimental analysis, as the session frequency increases, the proposed SDIS maximizes recommendation by 15.1%, the data analysis ratio by 9.41%, and reduces the modification rate by 17%.

Current Stance on Requirement Prioritization Techniques: Challenges, Opportunities, and Future Directions

Requirement Engineering is an important aspect of Software Engineering, while Requirement prioritization is a crucial phase of Requirement Engineering, specifically in agile development. In today's world, software systems are nothing but the backbone for business organizations. However, for business operations, requirements are changing rapidly, and it seems to be impossible to accomplish the requirements at once. It may be possible with the help of requirement Prioritization as it decreases the risk for failure. It helps implement the most required and prioritized feature given by stakeholders. The entire purpose of this research is to investigate and rate the complete process of Requirement Engineering, specifically the current requirement prioritization techniques, by conducting an extensive literature review. Furthermore, to identify the opportunities, gaps, and challenges for future

Prediction of visual field defects from macular optical coherence tomography in glaucoma using cluster analysis.

PurposeTo assess the accuracy of cluster analysis‐based models in predicting visual field (VF) defects from macular ganglion cell‐inner plexiform layer (GCIPL) measurements in glaucomatous and healthy cohorts.MethodsGCIPL measurements were extracted from posterior pole optical coherence tomography (OCT), from locations corresponding to central VF test grids. Models incorporating cluster analysis methods and corrections for age and fovea to optic disc tilt were developed from 493 healthy participants, and 5th and 1st percentile limits of GCIPL thickness were derived. These limits were compared with pointwise 5th and 1st percentile limits by calculating sensitivities and specificities in an additional 40 normal and 37 glaucomatous participants, as well as applying receiver operating characteristic (ROC) curve analyses to assess the accuracy of predicting VF results from co‐localised GCIPL measurements.ResultsClustered models demonstrated globally low sensitivity, but high specificity in the glaucoma cohort (0.28–0.53 and 0.77–0.91, respectively), and high specificity in the healthy cohort (0.91–0.98). Clustered models showed similar sensitivities and superior specificities compared with pointwise methods (0.41–0.65 and 0.71–0.98, respectively). There were significant differences in accuracy between clusters, with relatively poor accuracy at peripheral macular locations (p < 0.0001 for all comparisons).ConclusionsCluster analysis‐based models incorporating age correction and holistic consideration of fovea to optic disc tilt demonstrated superior performance in predicting VF results to pointwise methods in both glaucomatous and healthy eyes. However, relatively low sensitivity and poorer performance at the peripheral macula indicate that OCT in isolation may be insufficient to predict visual function across the macula accurately. With modifications to criteria for abnormality, the concepts suggested by the described normative models may guide prioritisation of VF assessment requirements, with the potential to limit excessive VF testing.

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.

Multi-criteria and Multi-expert Requirement Prioritization using Fuzzy Linguistic Labels

Requirement prioritization in Software Engineering is the activity that helps to select and order for the requirements to be implemented in each software development process iteration. Thus, requirement prioritization assists the decision-making process during iteration management. This work presents a method for requirement prioritization that considers many experts' opinions on multiple decision criteria provided using fuzzy linguistic labels, a tool that allows capturing the imprecision of each experts' judgment. These opinions are then aggregated using the fuzzy aggregation operator MLIOWA considering different weights for each expert. Then, an order for the requirements is given considering the aggregated opinions and different weights for each evaluated dimension or criteria. The method proposed in this work has been implemented and demonstrated using a synthetic dataset. A statistical evaluation of the results obtained using different t-norms was also carried out.

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.

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.

Not All Requirements Prioritization Criteria are Equal at All Times: A Quantitative Analysis

Requirement prioritization is recognized as an important decision-making activity in requirements engineering and software development that is applied to determine which requirements should be implemented and released. In order to prioritize requirements, there are several approaches/techniques/tools that use different requirements prioritization criteria, which are often identified by gut feeling instead of an in-depth analysis of which criteria are most important to use. Therefore, in this study we investigate which requirements prioritization criteria are most important to use in industry when determining which requirements are implemented and released, and if the importance of the criteria change depending on how far a requirement has reached in the development process. We conducted a quantitative study of one completed project from one software developing company by extracting 83,408 requirements prioritization decisions based on eight requirements prioritization criteria for 11,110 requirements. The results show that not all requirements prioritization criteria are equally important, and this change depending on how far a requirement has reached in the development process.

Model planiranja logističke podrške u uslovima ograničenih resursa

Introduction/purpose: The paper presents a model of logistics support planning in the conditions of limited logistic resources based on the prioritization of customer requirements and resource allocation. Decisionmakers play a crucial role in the efficient and equitable allocation of resources as they prioritize among different user requirements. Methods: Requirement prioritization techniques that use nominal scale, ordinal scale, and ratio scale, and five methods for converting ranks into weighting coefficients have been applied to determine the degree of significance of user requirements. The Requirements triage method has been used for establishing relative priorities, while the heuristic algorithm determining the Kemeny median was used to consolidate individually ranked requests into a group rank. In order to balance opposing demands of users, consensus measures of group decision making were used. For obtaining an optimal planned solution of logistic support, the methods and techniques of resource allocation were applied. Results: A model for adaptive planning of logistics support in the conditions of limited resource capacities of the logistics system has been developed. Conclusion: The proposed model can be effectively applied in other areas of resource allocation.

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.

Agile project management challenge in handling scope and change: A systematic literature review

Uncertainty has become a challenge in project management during the age of the Covid-19 pandemic. Project management required a more flexible and dynamic approach; therefore, it is believed that Agile project management could provide a solution to this problem. Agile has been widely used in multiple industry areas as a practical and flexible approach in project management practices. In the project, the scope is considered one aspect that can directly affect the budget and timing. Unfortunately, it seems to be one of the most neglected domains in Agile and conventional. Agile is recognized for its rapid improvement as well as its willingness to embrace change. However, uncontrolled change can potentially delay and make the project overspend. This paper aims to examine the challenges and discover the best practices in handling scope and change in Agile. A systematic literature review (SLR) was carried out through the literature sources, which entailed IEEE Explore, ScienceDirect, Emerald Insight, ProQuest, and Wiley Online. The finding was seven categories, and eighteen issues were found. Then twenty-four best practices were mapped to the challenges. We found the most significant challenge came from People & Organization, User Requirement Prioritization, Over-scope Requirement, and Communication & Coordination. Those areas are considered the most significant challenge because they can create delays and overspend in the project.

Improving the Analytic Hierarchy Process for Requirements Prioritization Using Evolutionary Computing

Improving the Analytic Hierarchy Process for Requirements Prioritization Using Evolutionary Computing

CSOP+RP: a novel constraints satisfaction model for requirements prioritization in large-scale software systems

One of the main factors in the failure of software projects is the lack of attention to their requirements prioritization. In this paper, we propose a decision-oriented methodology with a novel model for requirements prioritization (RP) in large-scale software systems. The model is formulated based on the constraint satisfaction optimization problems (CSOP) approach, which we call CSOP+RP. The main objective of the model is to maximize the quality of the software in total, subject to the constraints on the budgets and importance level that pre-determined by the administrator. To evaluate CSOP+RP, we applied it to the police command-and-control system (PCCS), which is extensively used during the outbreak of the Coronavirus disease as an incident in terms of quality and speed of service. The results of various experiments show that the proposed model with its specific capabilities can find reasonable and a solution near optimal. Moreover, the sensitivity analysis indicates that the model is very sensitive to its parameters. Although, we applied the model to CCPS, the CSOP+RP is very general so that it can be applied to different types of software projects. Additionally, the model could be extended to other aspects and criteria of the RP problem and could play definite roles for optimal management of system resources.

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.