RePizer: a framework for prioritization of software requirements

The standard software development life cycle heavily depends on requirements elicited from stakeholders. Based on those requirements, software development is planned and managed from its inception phase to closure. Due to time and resource constraints, it is imperative to identify the high-priority requirements that need to be considered first during the software development process. Moreover, existing prioritization frameworks lack a store of historical data useful for selecting the most suitable prioritization technique of any similar project domain. In this paper, we propose a framework for prioritization of software requirements, called RePizer, to be used in conjunction with a selected prioritization technique to rank software requirements based on defined criteria such as implementation cost. RePizer assists requirements engineers in a decision-making process by retrieving historical data from a requirements repository. RePizer also provides a panoramic view of the entire project to ensure the judicious use of software development resources. We compared the performance of RePizer in terms of expected accuracy and ease of use while separately adopting two different prioritization techniques, planning game (PG) and analytical hierarchy process (AHP). The results showed that RePizer performed better when used in conjunction with the PG technique.     

Interactive requirements prioritization using a genetic algorithm

ContextThe order in which requirements are implemented affects the delivery of value to the end-user, but it also depends on technical constraints and resource availability. The outcome of requirements prioritization is a total ordering of requirements that best accommodates the various kinds of constraints and priorities. During requirements prioritization, some decisions on the relative importance of requirements or the feasibility of a given implementation order must necessarily resort to a human (e.g., the requirements analyst), possessing the involved knowledge.ObjectiveIn this paper, we propose an Interactive Genetic Algorithm (IGA) that includes incremental knowledge acquisition and combines it with the existing constraints, such as dependencies and priorities. We also assess the performance of the proposed algorithm.MethodThe validation of IGA was conducted on a real case study, by comparing the proposed algorithm with the state of the art, interactive prioritization technique Incomplete Analytic Hierarchy Process (IAHP).ResultsThe proposed method outperforms IAHP in terms of effectiveness, efficiency and robustness to decision maker errors.ConclusionIGA produces a good approximation of the reference requirements ranking, requiring an acceptable manual effort and tolerating a reasonable human error rate.     

Practical challenges of requirements prioritization based on risk estimation

Requirements prioritization and risk estimation are known to be difficult. However, so far, risk-based requirements prioritization has not been investigated empirically and quantitatively. In two quantitative experiments, we explored practical challenges and needs of risk estimations in general and of our method MOQARE specifically. In the first experiment, ten students made individual estimations. In the second one, twenty-four students estimated risks in seven moderated groups. The students prioritized the same requirements with different methods (risk estimation and ranking). During the first experiment, we identified factors which influence the quality of the prioritization. In the second experiment, the results of the risk estimation could be improved by discussing risk estimations in a group of experts, gathering risk statistics, and defining requirements, risks and prioritization criteria more tangibly. This first quantitative study on risk-based requirements prioritization helps to understand the practical challenges of this task and thus can serve as a basis for further research on this topic.     

Tool-supported requirements prioritization: Comparing the AHP and CBRank methods

Requirements prioritization aims at identifying the most important requirements for a software system, a crucial step when planning for system releases and deciding which requirements to implement in each release. Several prioritization methods and supporting tools have been proposed so far. How to evaluate their properties, with the aim of supporting the selection of the most appropriate method for a specific project, is considered a relevant question.In this paper, we present an empirical study aiming at evaluating two state-of-the art tool-supported requirements prioritization methods, AHP and CBRank. We focus on three measures: the ease of use, the time-consumption and the accuracy. The experiment has been conducted with 23 experienced subjects on a set of 20 requirements from a real project. Results indicate that for the first two characteristics CBRank overcomes AHP, while for the accuracy AHP performs better than CBRank, even if the resulting ranks from the two methods are very similar. The majority of the users found CBRank the “overall best” method.     

A systematic literature review of software requirements prioritization research

ContextDuring requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases. It is a major step taken in making crucial decisions so as to increase the economic value of a system.ObjectiveThe purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions.MethodSearch terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins.Results73 Primary studies were selected from the search processes. Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers. Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted to 13.ConclusionPrioritization has been significantly discussed in the requirements engineering domain. However, it was generally discovered that, existing prioritization techniques suffer from a number of limitations which includes: lack of scalability, methods of dealing with rank updates during requirements evolution, coordination among stakeholders and requirements dependency issues. Also, the applicability of existing techniques in complex and real setting has not been reported yet.     

The effect of system feedback and decision context on value-based decision-making behavior

Relatively few studies in MIS research have examined systems to support value-based decision-making behavior. The increasing complexity of the decision environment necessitates more reliance on personal values by decision-makers, thus making it an important component to study when considering the design of systems to aid decision-making. This paper describes an exploratory experiment that was conducted to determine how individual value-based decision-making behavior can be influenced by an information system through the use of value specific feedback. It also examines the role of decision context on value-based decisions. The results indicate that value-based decision-making behavior can be influenced and discusses operant theory and reactance theory as useful predictors of decision-maker response to feedback in different decision contexts.     

A framework for prioritization of quality requirements for inclusion in a software project

Non-functional requirements (NFRs) determine the characteristics of a software product or service as a whole. The research described in this paper presents a quantitative framework involving respondents of both the project and the business organization, in order to determine the priority of a list of NFRs to be considered for implementation during a software development. The framework also provides a quantitative basis for evaluating the extent of value addition that can be achieved while deciding upon whether or not to consider a particular non-functional requirement for inclusion to the project’s requirement set. The assessment process also indicates the extent to which different business values are perceived important by representatives of business organizations, and their perception of the importance of the different NFRs. The work distinguishes from others by explicitly considering dependencies among NFRs in the evaluation process. The final results are expected to be beneficial to both the business and the project organization by identifying and implementing the desired NFRs that contribute to business value in a cost-effective manner.     

Memory ordering: a value-based approach

Value-based replay enforces memory ordering by simply reexecuting load instructions in program order prior to commit, eliminating the need for associative search functionality from the load queue. The load queue can therefore be implemented as a first-in-first-out buffer, like the reorder buffer, which is fundamentally more scalable and power-efficient. In order to mitigate the bandwidth and resource occupancy costs of replay, a set of heuristics filter the set of loads that must be replayed, resulting in negligible performance degradation and data cache bandwidth increases relative to a conventional machine     

Optima: a domain-specific model for prioritization and conflicts management in requirements engineering for services intermediaries

New business models are set up, thanks to Web technologies. In this work, we focus on services intermediary companies. They generate value through the (automatic) selection of third-party services and the (automatic) delivery of the combinations of these services to consumers. Such companies face the problem of deciding which services to select and deliver in order to maximize their profit. The two main paper objectives are (i) to design the generic business model of services intermediaries and (ii) to propose an optimization model. The latter enables to choose the consumer requirements that will be satisfied in order to maximize profit. This model ranks implementable solutions based on various financial aspects. They are related to cost and revenue information that is associated with the requirements. It can support the decision-making process that aims at selecting a profit-maximizing set of requirements for services intermediaries’ system-to-be. Indeed, the proposed model solves the conflicts between requirements and prioritizes the optional requirements. We argue for the relevance of the optimization model via an example and simulations.     

Designing and assessing a course on prioritization and importance assessment in strategic non-routine requirements engineering processes

In this contribution, we present a course for making stakeholders in requirements engineering (RE) processes aware of the relevance of importance assessment (the thinking process that they go through while assessing requirement weights) and giving them some experience with specific aspects of the importance assessment process. We also analyze the experiences of the participants in five pilot sessions. In RE instruments, elicitation of requirement weights receives much attention. However, the processes that individual stakeholders go through while assessing weights are largely ignored or seen as a ‘black box’. In the course, participants gain experience with some common issues and pitfalls in assessing weights. Issues covered are: completeness and interdependence of requirements, causal relationships and the common denominator, handling ‘irrational’ requirements, and the meaning of ‘importance’ (priority). The course was given in various large organizations in the aerospace sector, and data on participants’ experiences were gathered by means of a standardized questionnaire. The extent to which the participants claimed they learned about the relevance of importance assessment and about how to perform it were, respectively, 2.89 and 2.72 on a scale from 1 to 5. The relevance of the various assignments was rated between 3.74 and 4.00 on a 1–5 scale. Our study indicates that the course, or elements of it, should be embedded in an organization’s work practices in order to achieve lasting effects.     

Simulating mixed agile and plan-based requirements prioritization strategies: proof-of-concept and practical implications

In this paper, we address the efficacy and pragmatics of mixing two primary strategies for requirements prioritization in order to incorporate the benefits of both plan-based (PB) and agile development methods while avoiding their drawbacks. As it is intractable to directly study the performance of strategies on real projects, we conducted a comprehensive empirically based simulation under a variety of conditions of requirements dynamism, project size, and duration. Simulation results suggest that a mixed strategy for requirements prioritization seems to work best in all but cost for typical levels of dynamism on average. Our findings also indicate that, as theorized, PB and agile strategies perform well within opposite extremes of dynamism. However, they do not outperform the mixed strategies even within their home grounds – that is large and complex systems with stable requirements for PB, and small and dynamic projects for agile methods. Given the unknown, unknowable, or variable nature of dynamism and the dramatic differences in effectiveness for agile and PB strategies under extreme development scenarios, a mixed strategy appears to yield the best results overall. We introduce two mixed strategies – simply adding cost–benefit (CB) to the agile approach, and a more sophisticated ‘hybrid’ (HY) approach that modulates development iteration size to maximize the expected CB for each iteration. We propose a step-by-step method to implement this HY strategy. We provide a structured analysis of the benefits and assumptions of agile and PB requirements prioritization methods (e.g., Pareto optimization), and outline a framework for analyzing and assessing the effectiveness of strategies including several new metrics. This research can furthermore serve as a framework for future validation of the proposed mixed strategies using actual software projects.     

Investigating the penalty reward calculus of software users and its impact on requirements prioritization

ContextThe current requirements engineering techniques for prioritization of software requirements implicitly assume that each user requirement will have an independent and symmetric impact on user satisfaction. For example, it is assumed that implementing a high priority user requirement will positively impact his satisfaction and not implementing a high priority user requirement will negatively impact his satisfaction. Further, the impacts of implementing multiple user requirements on his satisfaction are expected to be additive. But is this always the case?ObjectiveThis paper empirically examines whether the assumptions of symmetric and multiplicative impacts of user requirements on his satisfaction are valid. Further, the study assesses the relative efficacy of 5 methods of requirements prioritization in managing these effects as reflected by the user satisfaction with the prioritized requirement sets.MethodTo test for existence and mitigation of asymmetric effects an adaptation of the widely accepted PRCA (Penalty Reward Contrast Analysis) method was used for 5 requirements prioritization techniques. To test for existence and mitigation of multiplicative effects MHMR (Moderated Hierarchical Multiple Regression) a well-accepted technique for testing interaction effects was used.ResultsBoth asymmetric and multiplicative effects of software requirements on user satisfaction were observed for requirements prioritized using all 5 requirements prioritization methods raising questions about the efficacy of present day requirements prioritization techniques. Further, the results of the experiment led to proposing a new method for requirements prioritization for managing these effects.ConclusionThe study empirically demonstrates the complexities of prioritizing software requirements and calls for a new generation of methods to address them. Understanding and resolving these complexities will enable software providers to conserve resources by enabling them to parsimoniously selecting only those requirements for implementation in the software product that have maximum incremental impact on user satisfaction.     

The effect of prioritization on the behavior of a finite queue with batch Poisson inputs and a synchronous server

This paper studies the effect of prioritization on the behavior of a finite queueing system which accepts batch Poisson inputs and receives service from a synchronous server. Inputs are divided into twoclasses: ordinary and priority. Buffer is shared between ordinary and priority customers. Two buffer sharing strategies are considered in this paper: sharing with maximal length and complete sharing subject to purgation. An arriving batch can be partially accepted if its size exceeds the remaining buffer space. For each buffer sharing strategy, results obtained include state transition probability, blocking probability and delay.     

Test case prioritization: a systematic mapping study

Test case prioritization techniques, which are used to improve the cost-effectiveness of regression testing, order test cases in such a way that those cases that are expected to outperform others in detecting software faults are run earlier in the testing phase. The objective of this study is to examine what kind of techniques have been widely used in papers on this subject, determine which aspects of test case prioritization have been studied, provide a basis for the improvement of test case prioritization research, and evaluate the current trends of this research area. We searched for papers in the following five electronic databases: IEEE Explorer, ACM Digital Library, Science Direct, Springer, and Wiley. Initially, the search string retrieved 202 studies, but upon further examination of titles and abstracts, 120 papers were identified as related to test case prioritization. There exists a large variety of prioritization techniques in the literature, with coverage-based prioritization techniques (i.e., prioritization in terms of the number of statements, basic blocks, or methods test cases cover) dominating the field. The proportion of papers on model-based techniques is on the rise, yet the growth rate is still slow. The proportion of papers that use datasets from industrial projects is found to be 64 %, while those that utilize public datasets for validation are only 38 %. On the basis of this study, the following recommendations are provided for researchers: (1) Give preference to public datasets rather than proprietary datasets; (2) develop more model-based prioritization methods; (3) conduct more studies on the comparison of prioritization methods; (4) always evaluate the effectiveness of the proposed technique with well-known evaluation metrics and compare the performance with the existing methods; (5) publish surveys and systematic review papers on test case prioritization; and (6) use datasets from industrial projects that represent real industrial problems.     

Overview of cyber security: a crisis of prioritization

The Internet, originally a development of the USA government, opened to commercial traffic in the early 1990s. Since then, its growth internationally has been phenomenal. In several nations, the Internet is now fundamental for communication, and it has become basic to society, including supporting several aspects of the USA national critical information infrastructure. Because the Internet was built as all information-sharing network, security did not figure prominently in its original architecture. This deployment is beneficial but it has also brought serious risks.     

Test case prioritization: a family of empirical studies

To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One potential goal of such prioritization is to increase a test suite's rate of fault detection. Previous work reported results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: 1) Can prioritization techniques be effective when targeted at specific modified versions; 2) what trade-offs exist between fine granularity and coarse granularity prioritization techniques; 3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? To address these questions, we have performed several new studies in which we empirically compared prioritization techniques using both controlled experiments and case studies     

Understanding requirement prioritization artifacts: a systematic mapping study

The importance of prioritizing requirements stems from the fact that not all requirements can usually be met with available time and resource constraints. Efficient and trustworthy methods for prioritizing requirements are therefore in high demand. In this article, we present results of a systematic mapping study in order to appreciate the different considerations that have influenced prioritization of software requirements, identify the various types of artifacts proposed toward prioritizing software requirements, and examine certain characterizations of these artifacts. The results emphasize the heightened attention the domain of requirement prioritization has received in recent years. On the basis of this study, we are able to provide the following inferences regarding possible future research trajectories in software requirement prioritization artifacts: (1) focus on frameworks and tools; (2) emphasis on specialization; and (3) proposition of theory-based artifacts. Additional research possibilities are also pointed out at the end and are expected to stimulate further research on the topic.     

The impacts of organizational culture on information security culture: a case study

Information security cannot rely solely on technology. More attention must be drawn to the users’ behavioral perspectives regarding information security. In this study, we propose that a culture encouraging employees to comply with information policies related to collecting, preserving, disseminating and managing information will improve information security. Information security culture is believed to be influenced by an organization’s corporate culture (or organizational culture). We examine how this occurs through an in-depth case study of a large organization. We present a relationship map for organizational culture and information security practices. Six propositions are drawn from the findings of our interviews and discussions. Managerial insights, such as how to measure an organization’s information security culture and subsequently determine what perspective(s) is important for the organization to improve, are also discussed.     

The effect of negative online consumer reviews on product attitude: An information processing view

Online consumer reviews provide product information and recommendations from the customer perspective. This study investigates the effects of negative online consumer reviews on consumer product attitude. In particular, it examines the proportion and quality of negative online consumer reviews from the perspective of information processing. The elaboration likelihood model is used to explain the persuasive effect of the proportion and quality depending on product involvement. A high proportion of negative online consumer reviews elicits a conformity effect. As the proportion of negative online consumer reviews increases, high-involvement consumers tend to conform to the perspective of reviewers, depending on the quality of the negative online consumer reviews; in contrast, low-involvement consumers tend to conform to the perspective of reviewers regardless of the quality of the negative online consumer reviews. The experiment in this study uses 248 college students in Korea. The proposed hypotheses are tested by three-way analysis of covariance.