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RSCID: requirements selection considering interactions and dependencies

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Abstract

Requirements selection is one of the essential aspects of requirement engineering. So far, a lot of work has been done in this field. But, it is difficult to choose the right set of software requirements, taking into account their interactions and dependencies and only a few researches have paid attention to interactions and dependencies between requirements. However, in this paper, an attempt has been made to provide a method by considering interactions and dependencies between requirements. To better manage these features, we have also improved the search-based methods used in this area. According to the proposed method called RSCID, before choosing the optimized subset of requirements, dependencies between requirements are extracted. In  the next step, an algorithm is proposed based on the NSGA-II method. In this algorithm, a hybrid fitness function is introduced in addition to two other functions that are used. To tradeoff between cost and value functions, user interactions are also deployed. Another algorithm is used in this paper to choose an appropriate requirements subset, the combination of the NSGA-II method and a genetic algorithm to obtain three fitness functions. The results of the proposed methods have been compared to other methods based on the evaluation criteria in this field. The experiments show the efficiency of the proposed methods to select efficient and useful requirements.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

    Mohammad Reza Keyvanpour

  2. Data Mining Lab, Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran

    Zahra Karimi Zandian & Elham Sodagari

Authors
  1. Mohammad Reza Keyvanpour
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  2. Zahra Karimi Zandian
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  3. Elham Sodagari
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Correspondence to Mohammad Reza Keyvanpour.

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Keyvanpour, M.R., Karimi Zandian, Z. & Sodagari, E. RSCID: requirements selection considering interactions and dependencies. Genet Program Evolvable Mach 26, 14 (2025). https://doi.org/10.1007/s10710-025-09511-y

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