Research on Technologies of Software Requirements Prioritization

Requirements prioritization (RP) is a crucial process which aims to evaluate candidate software requirements to be implemented in the next release of a software system. RP is often applied iteratively, according to various criteria, by multiple stakeholders, who may have different roles and needs. The problems encountered during the RP of large sets of requirements can be addressed by following a systematic large-scale decision-making (LSDM) process. The aim of this chapter is to propose an LSDM RP process which applies multi-criteria evaluation based on intuitionistic fuzzy sets (IFSs). IFSs are suitable to consider stakeholders hesitation for criteria importance and requirements ratings. The proposed process is supported by a recommender system which provides suggestions to stakeholders and also guide them to collaboratively reach consensus for the final RP. The proposed process is tested using an illustrative example. The results are promising since they demonstrate that the suggested process can effectively support multiple stakeholders to prioritize large requirements sets.

The objective of the study is to develop an algorithm for prioritizing requirements in the development of software for critical infrastructure object projects. Requirement development is a fundamental phase in any software project, as this phase involves the identification, processing, and manipulation of requirements. The primary source of these requirements is project stakeholders, taking into account project constraints and limits. The number of requirements varies for each software project for a critical infrastructure object, hence the term requirement prioritization pertains to determining the priority order of executing software requirements based on considerations and decisions of stakeholders. Various proposed optimization algorithms are employed to address optimization tasks. This paper presents the main stages of basic optimization algorithms, some of their modifications aimed at enhancing their efficiency in solving such types of problems. Additionally, a hybrid approach based on WOA and GWO optimization algorithms is proposed, combining the advantages of each algorithm to determine the priority of requirements for critical infrastructure object software. Furthermore, a dataset from the SKUDA project is provided, utilized in this research, meeting the requirements of a real software project for evaluating the proposed method. The scientific novelty lies in the modification, application, and combination of results from well-known GWO and WOA algorithms to address the requirement prioritization task for critical infrastructure object software projects. The proposed algorithm achieves an accuracy of 92% for the proposed set of requirements.

<p>Increase in globalization of the industry of software requires an exploration of requirements engineering (RE) in software development institutes at multiple locations. Requirements engineering task is very complicated when it is performed at single site, but it becomes too much complex when stakeholder groups define well-designed requirements under language, time zone and cultural limits. Requirements prioritization (RP) is considered as an imperative part of software requirements engineering in which requirements are ranked to develop best-quality software. In this research, a comparative study of the requirements prioritization techniques was done to overcome the challenges initiated by the corporal distribution of stakeholders within the organization at multiple locations. The objective of this study was to make a comparison between five techniques for prioritizing software requirements and to discuss the results for global software engineering. The selected techniques were Analytic Hierarchy Process (AHP), Cumulative Voting (CV), Value Oriented Prioritization (VOP), Binary Search Tree (BST), and Numerical Assignment Technique (NAT). At the end of the research a framework for Global Software Engineering (GSE) was proposed to prioritize the requirements for stakeholders at distributed locations.<strong></strong></p>

As one of the gatekeepers of quality software systems, requirements’ prioritization (RP) is often used to select the most important requirements as perceived by system stakeholders. To date, many RP techniques that adopt various approaches have been proposed in the literature. To identify the strengths, opportunities, and limitations of these existing approaches, this paper studied and analyzed the RP field in terms of its significance in the software development process based on the standard review guidelines by Kitchenham. By a rigorous study selection strategy, 122 relevant studies were selected to address the defined research questions. Findings indicated that RP plays a vital role in ensuring the development of a quality system with defined constraints. The stakeholders involved in RP were reported, and new categories of the participating stakeholders were proposed. Additionally, 108 RP techniques were identified and analyzed with respect to their benefits, prioritization criteria, size of requirements, types in terms of automation level, and their limitations; 84 prioritization criteria were disclosed with their frequency usages in prioritizing the requirements. The study revealed that the existing techniques suffer from serious limitations in terms of scalability, the lack of quantification, and the prioritization of the participating stakeholders, time consumption, requirement interdependences, and the need for highly professional human intervention. These findings are useful for researchers and practitioners in improving the current state of the art and state of practices.

This paper studied customer relationship management system with decision support. At present, the fitness studio management system has accumulated a large amount of customer data, but the studio still has problems in customer management such as inaccurate customer target positioning and imperfect customer relationship management functions. It is still unable to conduct intelligent analysis, resulting in a decline in the overall management ability of customer groups. As such, customer turnover is high, and customer loyalty is reduced. To solve such problems, this research constructed a customer relationship management system with decision support, which can be used to identify customers with high studio and high platform loyalty, and with repeated purchase of GT fitness studio. With the purpose of helping the studio achieve accurate customer relationship management and business decision management, this study built an intelligent customer cluster analysis system through K-means clustering algorithm, which can automatically cluster customers into four (4) categories, indicate the characteristics of consumer behavior under each category, and screen out high-quality fitness customers with large consumption amount, high consumption frequency, high platform evaluation, high loyalty, and heavy consumption. At the same time, it also screens out general customers. Moreover, the intelligent group display of the clustered customers is visualized in the form of reports, and the visual display of each group is also carried out, so that decision makers can conveniently implement accurate group management of different groups of customers.

Background: Requirement prioritization plays key role in software development process. It is essential to prioritize the requirements for making the correct decision for either a single or multiple release of a product. In this paper we performed a systematic analysis on some of the significant factors like importance of requirements, risks, cost and time in context of requirement prioritization. Objective: With benefits these prioritization methods also have some limitations and shortcoming that are brought up in this paper. Stakeholders, managers, developers or their representatives make decisions for prioritization of requirements. Many techniques are analyzed how to manage these prioritizations considering general goals and limitations. Method: For the identification and analyzing of research articles published during 2009-2017, Systematic Literature Review based method is used in this paper. Results: In recent researches 40 different requirement prioritizations techniques has been used. This SLR also shows the major research gaps regarding techniques and tools for software requirements prioritization. Conclusion: This research shows the major prioritization techniques and tools for requirements elicitation. Tools & techniques identified in this study will assist future researchers to expend their views in the context. Moreover, it will help requirement engineers and practitioners to choose requirement prioritization techniques and tools according to their needs.

Intense competition in the business sector motivates every company to manage services to regular consumers to the fullest. Increase customer loyalty can be done by grouping customers into several groups and determine appropriate and effective marketing strategies for each group. This study aims to propose the right targeting of discounts that can increase customer loyalty in the retail business. Customer grouping uses data mining techniques with the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, which is divided into six phases, namely business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The formation of this cluster uses k-means clustering method and is based on RFM (recency, frequency, monetary) analysis. From the results of the silhouette test on 2734 transaction data from 210 customers of PT. XYZ from October 2019 to March 2020, three customer clusters were formed. From these three clusters, one cluster that has the best frequency and monetary values is chosen so that it is considered the worthiest group to be given a discount in order to maintain its loyalty.

Requirement prioritization is very useful for making decisions about product plan but most of the time it is ignored. In many cases it seems that the product hardly attains its principal objectives due to improper prioritization. Increased emphasis on requirement prioritization and highly dynamic requirements makes management of composite services time consuming and difficult task. When software project has rigid timelines, limited resources, but high client expectations, an instantaneous deployment of most vital and critical features becomes mandatory. The problem can be solved by prioritizing the requirements. Over the past years, various techniques for requirement prioritization are presented by a variety of researchers in software engineering domain. The proposed Adaptive Fuzzy Hierarchical Cumulative Voting (AFHCV) uses adaptive mechanism with existing Fuzzy Hierarchical Cumulative Voting (FHCV) technique, in order to increase the coverage of events that can occur at runtime. The adaptive mechanism includes Addition of new requirement set, Analysis and Reallocation of requirements, Assignment and Alteration of priorities and Re-prioritization. The re-prioritization is used to improve the results of proposed AFHCV. The proposed system compares the results of proposed AFHCV technique to the existing FHCV technique and the comparison shows the proposed AFHCV yields better results than FHCV.

The chapter gives a hybrid approach for prioritization of software requirements by considering regression testing efforts as one of the criteria. The prioritization is performed against the prioritized decision aspects. The chapter gives software engineers a technique for prioritizing both requirements and aspects. This technique considers regression testing effort as one of important parameters during requirement prioritization thereby reducing the regression testing effort. This technique is applied on live web application of “virtual classroom” and the results were promising. The technique resulted in decision aspect prioritization thereby leading to effective requirement prioritization and lower regression testing efforts.

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.

The goal of any automated requirement prioritization process is to reduce the difference between the true and estimated ranking. It is arguable that to what extent, using stakeholders' feedback will reduce that difference. Moreover, incorporating requirement ranking functions such as value, cost, risk, etc. along with the feedback is another concern. This paper proposes a scheme for requirement prioritization addressing the feedback issue along with a highly significant ranking function that have decisive impact on true ranking. The approach initially creates partial orders on the basis of stakeholders' feedback and then merges those orders using binary search technique. Two variations of proposed method were implemented varying the number of elicited requirement pairs which were ordered based on stakeholders' preference. The variations were analyzed on the basis of displacement for each requirement from true to estimated rank. Results indicate that for projects having significant ranking functions, decreasing stakeholders' feedbacks could help in reducing the ranking difference.

Requirement engineering is one of the most significant phases of software engineering. Success or failure of any software project relies heavily on better requirement engineering process. Better awareness of the requirements is fundamental for requirements engineering. Requirement Prioritization is an important component of requirement engineering process. In this paper, we have highlighted some serious shortcomings related to existing requirement prioritization techniques. Based on these findings, we have proposed an intelligent fuzzy logic based technique for requirements prioritization based on the perceived value of each requirement. We have also proposed a framework for evaluation of existing as well as proposed requirement prioritization techniques.

Traditional methods for requirements prioritization (RP) are currently limited by scalability and lack of automation issues. In recent years, there has been an exponential growth in the use of Artificial Intelligence (AI) techniques in different areas of software engineering (e.g., requirements analysis, testing, maintenance). In particular, I have found thirteen RP methods applying AI techniques such as machine learning, or genetic algorithms. 38% of these approaches seek to improve the scalability problem, whereas only 15% of them aim to improve the automation aspect along the RP process. Moreover, all these studies have carried out their evaluations with a number of requirements no greater than 100.In order to address the issues of scalability and lack of automation in RP, the present research project aims to propose a semi-automatic multiple-criteria prioritization method for functional and non-functional requirements of software projects developed within the Software Product-Lines paradigm. The proposed RP method will be based on the combination of Natural Language Processing techniques and Machine Learning algorithms, and for its validation, empirical studies will be carried out with real web-based geographic information systems (GIS). This paper describes the problem and technical challenges to be addressed, the related works, as well as the main contributions of the proposed solution.

Most software projects have more candidate requirements than can be realised within the time and cost constraints. This paper presents a novel multi-criteria decision analysis process to prioritise requirements. The novelty of the presented idea is three-fold. Firstly, to prioritise requirements, it distinguishes two categories of requirements according to their level of abstraction: low-level requirements and high-level requirements. Then, it relates business goals to the requirements of low-level which contribute to their fulfilment. This will improve the completeness and traceability of requirements. Secondly, requirements prioritisation is based on their degree of contribution to the identified business goals and their importance. So, business goals become the evaluation criteria. Finally, this process takes into account relationships and dependencies that may exist between business goals. At this end, we employ analytic hierarchy process (AHP) method to give weights to business goals and Choquet integral to calculate a global score, i.e. priority, for requirements.

In today’s competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of things (IoT) and employs a two-part modeling approach tailored to this digital era. In the first phase, we leverage the power of the self-organizing map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust decision tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the classification and regression tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, enhancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluates the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT customer relationship management (IoT-CRM) dataset but also provides valuable visualisations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research offers a significant contribution to businesses seeking to optimize their IoT-CRM strategies and capitalize on the opportunities presented by the IoT ecosystem.