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

Perini, Anna; Ricca, Filippo; Susi, Angelo

doi:10.1016/j.infsof.2008.12.001

Cited by 76 publications

(53 citation statements)

References 12 publications

(29 reference statements)

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“…Closely related to understanding the utility of features, the requirement engineering community has significantly contributed to the development of requirement prioritization techniques for requirements selection [42,43]. In this context, one suggestion for prioritizing requirements has been the use of requirement priority groups [44] as a way to nest similar requirements together and create an internal rank for requirements in each group.…”

Section: Related Workmentioning

confidence: 99%

Dynamic decision models for staged software product line configuration

Bagheri

Ensan

2013

Requirements Eng

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Abstract. Software product line engineering practices offer desirable characteristics such as rapid product development, reduced time-to-market and more affordable development costs as a result of systematic representation of the variabilities of a domain of discourse that leads to methodical reuse of software assets. The development lifecycle of a product line consists of two main phases: domain engineering, which deals with the understanding and formally modeling of the target domain; and application engineering that is concerned with the configuration of a product line into one concrete product based on the preferences and requirements of the stakeholders. The work presented in this paper focuses on the application engineering phase and builds both the theoretical and technological tools to assist the stakeholders in (1) understanding the complex interactions of the features of a product line; (2) eliciting the utility of each feature for the stakeholders and hence exposing the stakeholders' otherwise implicit preferences in a way that they can more easily make decisions; and (3) dynamically building a decision model through interaction with the stakeholders and by considering the structural characteristics of software product line feature models, which will guide the stakeholders through the product configuration process. Initial exploratory empirical experiments that we have performed show that our proposed approach for helping stakeholders understand their feature preferences and its associated staged feature model configuration process is able to positively impact the quality of the end results of the application engineering process within the context of the limited number of participants. In addition, it has been observed that the offered tooling support is able to ease the staged feature model configuration process.

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Section: Related Workmentioning

confidence: 99%

Dynamic decision models for staged software product line configuration

Bagheri

Ensan

2013

Requirements Eng

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“…They develop a multi-criteria decision model to support the distributed team work allocation decision by using decision conferencing and multi-attribute value analysis. Finnie et al [26] use AHP to prioritize software development productivity factors, and Karlsson et al [9] and Perini et al [27] compare AHP with other alternative method in prioritizing software requirements. Yoo et al [11] use AHP to improve test case prioritization techniques by employing expert knowledge, and compare the proposed approach with the conventional coverage-based test case prioritization technique.…”

Section: Background and Related Workmentioning

confidence: 99%

Adaptive Regression Testing Strategy: An Empirical Study

Arafeen

2011

2011 IEEE 22nd International Symposium on Software Reliability Engineering

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Abstract-When software systems evolve, different amounts and types of code modifications can be involved in different versions. These factors can affect the costs and benefits of regression testing techniques in different ways, and thus, there may be no single regression testing technique that is the most cost-effective technique to use on every version. To date, many regression testing techniques have been proposed, but no research has been done on the problem of helping practitioners systematically choose appropriate techniques on new versions as systems evolve. To address this problem, we propose adaptive regression testing (ART) strategies that attempt to identify the regression testing techniques that will be the most cost-effective for each regression testing session considering organization's situations and testing environment. To assess our approach, we conducted an experiment focusing on test case prioritization techniques. Our results show that prioritization techniques selected by our approach can be more cost-effective than those used by the control approaches.

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“…These comparisons grow with increase in the number of requirements [6]. State-of-the-art prioritization techniques such as AHP and CBRanks seem to demonstrate high capabilities [7]. These techniques have performed well in terms of ease of use and accuracy but, still lacking in scalability and rank reversals respectively.…”

Section: Introductionmentioning

confidence: 99%

A Clustering Based Technique for Large Scale Prioritization during Requirements Elicitation

Achimugu

Selamat

Ibrahim

2014

Advances in Intelligent Systems and Computing

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Abstract. We consider the prioritization problem in cases where the number of requirements to prioritize is large using a clustering technique. Clustering is a method used to find classes of data elements with respect to their attributes. KMeans, one of the most popular clustering algorithms, was adopted in this research. To utilize k-means algorithm for solving requirements prioritization problems, weights of attributes of requirement sets from relevant project stakeholders are required as input parameters. This paper showed that, the output of running k-means algorithm on requirement sets varies depending on the weights provided by relevant stakeholders. The proposed approach was validated using a requirement dataset known as RALIC. The results suggested that, a synthetic method with scrambled centroids is effective for prioritizing requirements using k-means clustering.

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

Cited by 76 publications

References 12 publications

Dynamic decision models for staged software product line configuration

Dynamic decision models for staged software product line configuration

Adaptive Regression Testing Strategy: An Empirical Study

A Clustering Based Technique for Large Scale Prioritization during Requirements Elicitation

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