Academic Journal
FEDA-NRP: A fixed-structure multivariate estimation of distribution algorithm to solve the multi-objective Next Release Problem with requirements interactions
| العنوان: | FEDA-NRP: A fixed-structure multivariate estimation of distribution algorithm to solve the multi-objective Next Release Problem with requirements interactions |
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| المؤلفون: | Pérez Piqueras, Víctor, Bermejo López, Pablo, Gámez Martín, José Antonio |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2023 |
| المجموعة: | Universidad de Castilla-La Mancha: Repositorio Universitario Institucional de Recursos Abiertos (RUIdeRA) |
| مصطلحات موضوعية: | Agile, Bayesian networks, Estimation of distribution algorithms, Evolutionary multi-objective search, Next Release problem, Search-based software engineering |
| الوصف: | In the development of a software product, the Next Release Problem is the selection of the most appropriate subset of requirements (tasks) to include in the next release of the product, such that the selected subset maximises the overall satisfaction of the stakeholders and minimises the total cost. Furthermore, in most cases, requirements or tasks cannot be developed independently, as there are dependencies between them, which must be respected in the selection for the next release. In this paper, we approach the Next Release Problem as a constrained bi-objective optimisation problem. The main contribution is the design of an Estimation of Distribution Algorithm that exploits domain knowledge, i.e. the dependencies between the requirements, to define the structure of a Bayesian network that models the relationships between the binary variables (requirements) to be optimised. The use of a Bayesian network with a fixed structure reduces the complexity of the search process, since it is unnecessary to learn the structure at each iteration of the algorithm. Moreover, it ensures that the sampled individuals are always valid with respect to the required dependencies. The second main contribution is the generation of a corpus of synthetic datasets with cost estimations derived from agile and classic management methodologies. Standard multi-objective metrics are computed in order to assess our proposal and compare it with other evolutionary multi-criterion optimisation algorithms, determining that it is the optimal choice when dealing with complex datasets. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | SBPLY/21/180225/000062; 2023-GRIN-34437; MCIN/AEI/10.13039/501100011033; PID2019–106758GB–C33; Engineering Applications of Artificial Intelligence. 2023, 124, 0-0; https://hdl.handle.net/10578/33011 |
| DOI: | 10.1016/j.engappai.2023.106555 |
| الاتاحة: | https://hdl.handle.net/10578/33011 https://doi.org/10.1016/j.engappai.2023.106555 https://dl.acm.org/doi/10.1016/j.engappai.2023.106555 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.1B332A97 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.engappai.2023.106555 |
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