Requirement Prioritization Using Adaptive Fuzzy Hierarchical Cumulative Voting

In any software systems, the requirements prioritization considered as pivotal task. This paper aims to explain and discuss the works done on requirements prioritization based Machine learning along with the dependency-aware requirements prioritization. Machine learning has become of attention to scientists, researchers, and users because of the existence of vast data and deep learning algorithms that can analyze massive sets of data. The basic algorithms are used for dependency learning calculation, resolve the stakeholder’s conflicts, classify requirements, and scalability improvement. This paper will present a brief background and comprehensive presentation of a number of machine learning techniques for requirements prioritization and those concerning the requirement dependency in its simple, complex, and Hybrid form. There are a number of papers, articles, and research papers that deal with requirements prioritization, few of them handling the dependency. This paper will present a brief background for several requirements prioritization based on machine learning. then make a comparison and discussion of a number of selected techniques in terms of algorithm type, issues addressed, and evaluated data level. method that handling the dependency regarding of strengths and weakness

Test case prioritization technique aims to improve test efficiency rate by sorting test cases according to some specific criteria. Requirements play an important role throughout software testing. This paper proposes a test case prioritization method based on requirement correlations. Prioritization of requirements is defined by the users and the developers. This technique focuses on requirements with detected faults after the last regression testing. By readjusting prioritization of fault-related requirements, it can optimize the order of test cases. Experimental results show that this technique exactly contributes to achieving high testing efficiency.

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.

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.

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.

A Supporting Tool for Requirements Prioritization Process in Agile Software Development

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.

This paper proposes a new Non functional prioritization technique that uses the weighted average of stakeholder weight and their preferences per aspects. The change in usage count and/or dependencies leads to change in priority of requirements and finally the average change in priority are calculated each time there is change in priority. The new project requirements are mapped with existing similar projects, in case of directly mapped, the priorities of similar projects are modified by adding the average change in priority and then assigned as priority of directly mapped requirement of new project. For different requirements, the requirements are prioritized using the weighted average of stakeholder weight and their preferences along with usage and dependency count. The proposed technique is compared with existing non functional requirement prioritization technique and former technique out performs the latter in terms of accuracy and time of computation due to heavy reliance on values of historical similar projects.

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.

Criteria analysis in requirement prioritization should always be transparent and holistic to increase the stakeholder’s satisfaction. Different stakeholders have their views on the same criteria, making it challenging to agree on a specific set of criterion weights. In software development, collaboration between client and developer is needed for requirements to be achieved. The cost-value approach to determining priority requirements requires a client perspective to determine the value and developer’s concept to ascertain costs, then form a cost-value diagram for analysis. Therefore, the research conducted has not been detailed using a cost-value approach involving a client perspective and a developer concept. Usually, the criteria and alternatives weighted by the decision-maker are vague, uncertain, and subjective. Furthermore, using fuzzy numbers help solve different types of uncertainty and conflicting requirements to produce reliable work. This study proposes increasing the weighting of criteria in the requirements prioritization technique, based on cost-value, by doing a collaboration perspective through Fuzzy AHP and TOPSIS using linguistic terms by presenting a proposed model.

In requirements engineering (RE), different activities are carried on to add up business values to the software products. In value-base software (VBS), financial impacts on business are measured, and this phenomenon makes VBS different than other traditional software applications. In VBS systems, determination of valuable requirements is a crucial task and depends on multiple aspects or factors. Requirement prioritization is a part of RE and is considered as a crucial decision-making activity. Requirements prioritization is a process where requirements are arranged in an order to create a stakeholder priority list. Different requirements prioritization techniques are used by the experts in the industry to partially support the prioritization process for VBS development. Business and technical aspects play a vital role to make prioritization process effective. In existing requirements prioritization techniques, technical aspects are not applied well, and their consideration is partial only. Moreover, the business aspects are ignored which are highly vital for VBS development. There is a need to introduce multi-aspects requirements prioritization technique to support the selection of candidate requirements for VBS development. Hence, this paper focuses on a new requirements prioritization technique for VBS.

Agile methodology is an innovative and iterative process that supports changing requirements and helps in addressing changes throughout the development process. Requirements are elicited at the beginning of every software development process and project (product) and latter are prioritized according to their importance to the market and to the product itself. One of the most important and influencing steps while making a software product is requirements prioritization. Prioritizing requirements helps the software team to understand the existence and importance of a particular requirement, there are several requirements prioritization methodologies that aid in decision making but importantly many lacks to account the important factors that have significant influence in prioritizing requirements. We have proposed a requirement prioritization methodology based on several factors such as time to market, cost, risk etc. The proposed model is expected to overcome this lack. In (Scrum) sprints, requirements will be prioritized both on the basis of influencing factors such as cost, value, risk, time to market etc. and through the effect of non-functional requirements over functional requirements. This will improve the overall quality of software product when it is included in the development process of scrum. Requirements will not only be prioritized based on sprints, human decision but by critically analyzing the factors (sub characteristics) that can cause the product to success/ fail repeatedly thus ensuring the consistency in right requirements and hence the right prioritized requirements will be selected for a particular sprint at a time.

<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>

This chapter provides an overview of techniques for prioritization of requirements for software products. Prioritization is a crucial step towards making good decisions regarding product planning for single and multiple releases. Various aspects of functionality are considered, such as importance, risk, cost, etc. Prioritization decisions are made by stakeholders, including users, managers, developers, or their representatives. Methods are for combining individual prioritizations based on overall objectives and constraints. A range of different techniques and aspects are applied to an example to illustrate their use. Finally, limitations and shortcomings of current methods are pointed out, and open research questions in the area of requirements prioritization are discussed.

Requirements prioritization is one of the key factors in deciding the success of the project and hence the software industry. One of the major concerns in software prioritization techniques is that the existing ranking techniques have a very modest support to different criteria used by stakeholders to present their ranking. The current techniques are not suitable for arriving at anoptimized view of multiple stakeholders using multiple criteria. This research analyzes the issues in existing techniques. A web based decision support model using ELECTRE as the method for prioritization is proposed. ELECTRE is a multi-criteria decision making model that is proved to be effective in ranking several decision making problems. The proposed system takes input from multiple stakeholders using 100-point method. An optimized ranking is obtained using ELECTRE method. The developed system is validated using a pilot project and is found to be efficient in terms of saving cost of implementation and man-hours needed for implementation.