Interactive requirements prioritization using a genetic algorithm

Tonella, Paolo; Susi, Angelo; Palma, Francis

doi:10.1016/j.infsof.2012.07.003

Cited by 98 publications

(54 citation statements)

References 10 publications

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“…( Tonella et al, 2013) proposed an Interactive Genetic Algorithm (IGA) for requirements prioritization, in their work they take into consideration the effectiveness, efficiency, and robustness to the errors that may occurs in decision making process. As they assessed their technique they concluded that the IGA performs better than Incomplete Analytic Hierarchy Process (IAHP).…”

Section: Data Mining and Machine Learning Techniquesmentioning

confidence: 99%

Requirements Prioritization Techniques Comparison

Hudaib¹,

Masadeh²,

Qasem³

et al. 2018

MAS

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Requirements prioritization is considered as one of the most important approaches in the requirement engineering process. Requirements prioritization is used to define the ordering or schedule for executing requirement based on their priority or importance with respect to stakeholders' viewpoints. Many prioritization techniques for requirement have been proposed by researchers, and there is no single technique can be used for all projects types. In this paper we give an overview of the requirement process and requirement prioritization concept. We also present the most popular techniques used to prioritize the software project requirements and a compression between these techniques. On the other hand, we spot the light on the importance of involving the non-functional requirements prioritization because of the great effects of non-functional on project success and quality; some approaches that used in prioritize non-functional requirements are discussed in this paper, in addition a general model is proposed based on reviewing the prioritization techniques in order to suggests a best suited technique for specific projects according to decision makers parameters.

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Section: Data Mining and Machine Learning Techniquesmentioning

confidence: 99%

Requirements Prioritization Techniques Comparison

Hudaib¹,

Masadeh²,

Qasem³

et al. 2018

MAS

View full text Add to dashboard Cite

show abstract

“…In [3], a technique is presented for partially automating the prioritization of requirements and raw feature requests in large-scale elicitation processes. Another approach in [19] applied genetic algorithm for prioritizing requirements in order to make prioritization scalable by reducing the number of pairwise comparisons. However, no particular method or approach has been proposed for performing the prioritization of non-functional requirements according to their relationship with functional requirements.…”

Section: Related Workmentioning

confidence: 99%

“…Numerous methods on requirements prioritization have been introduced in recent years, the most widely known of which being Analytic Hierarchy Process (AHP) [16], Cost-Value approach [1], Wieger"s method [17], Value-Oriented requirements Prioritization (VOP) [18], and more recently there is a method which applied interactive genetic algorithm to prioritize requirements [19]. Although these methods have contributed a lot to software development process, they are not suitable for prioritizing non-functional requirements from the point that they have not addressed the prioritization of non-functional requirements according to their relationship with functional requirements.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Prioritizing NFRs According to Their Relationship with FRs

Dabbagh¹,

Lee²

2015

LNSE

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Abstract-Due to the budgetary deadlines and time to market constraints, it is essential to prioritize software requirements. The outcome of requirements prioritization is an ordering of requirements which need to be considered first during the software development process. To achieve a high quality software system, non-functional requirements (NFRs) must be taken into consideration throughout the prioritization process. Although several requirements prioritization methods have been proposed so far, the research on prioritizing non-functional requirements is limited. In this paper, we propose a quantitative approach based on Fuzzy Logic and Alpha cut approach which aims to perform the process of prioritizing non-functional requirements. Application of the proposed approach provides the practitioners with a prioritized list of non-functional requirements which are prioritized based on their relationship with functional requirements (FRs).

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“…Besides software testing, search-based optimization techniques have also been applied to fault localization [65], program analysis [76], software refactoring [29,30,55], cost estimation [19], project scheduling [1,18], decisions design optimization [10], automated negotiation [17], source code parallelization [57], requirement engineering [27,64], variability management [41], and so on.…”

Section: Search-based Software Engineeringmentioning

confidence: 99%

Search-based inference of polynomial metamorphic relations

Zhang

Chen

Hao

et al. 2014

Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering

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Metamorphic testing (MT) is an effective methodology for testing those so-called "non-testable" programs (e.g., scientific programs), where it is sometimes very difficult for testers to know whether the outputs are correct. In metamorphic testing, metamorphic relations (MRs) (which specify how particular changes to the input of the program under test would change the output) play an essential role. However, testers may typically have to obtain MRs manually.In this paper, we propose a search-based approach to automatic inference of polynomial MRs for a program under test. In particular, we use a set of parameters to represent a particular class of MRs, which we refer to as polynomial MRs, and turn the problem of inferring MRs into a problem of searching for suitable values of the parameters. We then dynamically analyze multiple executions of the program, and use particle swarm optimization to solve the search problem.To improve the quality of inferred MRs, we further use MR filtering to remove some inferred MRs.We also conducted three empirical studies to evaluate our approach using four scientific libraries (including 189 scientific functions). From our empirical results, our approach is able to infer many high-quality MRs in acceptable time (i.e., from 9.87 seconds to 1231.16 seconds), which are effective in detecting faults with no false detection.

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Interactive requirements prioritization using a genetic algorithm

Cited by 98 publications

References 10 publications

Requirements Prioritization Techniques Comparison

Requirements Prioritization Techniques Comparison

An Approach for Prioritizing NFRs According to Their Relationship with FRs

Search-based inference of polynomial metamorphic relations

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