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<p>The planning of a <b>s</b>oftware project includes the effort prediction (i.e., the number of person-hours) needed to complete one or more activities of the software development life cycle (SDLC). One of these activities corresponds to implementation. Effort is used for managers for budgeting software projects. Managers commonly take as reference the effort percentage of any SDLC activity with respect to the total SDLC effort. Regarding implementation activity, we found a range between 0.31 and 27.39% for new projects, and between 0.15 and 32.30% for maintained projects, which could cause uncertainty in managers. Therefore, we propose a new nature-inspired algorithm termed <i>Mexican Axolotl Optimization with Random Incorporation</i> (MAORI) as a general metaheuristic for predicting the implementation effort of projects. We embedded statistical regression equations (SRE) in the MAORI to obtain a MAORI-SRE algorithm. Results showed that MAORI-SRE prediction accuracy was better than SRE when optimized through particle swarm optimization, and genetic algorithms.</p>

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Axolotl inspired metaheuristic for software implementation effort prediction

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Cuauhtémoc López-Martín,
  • Yenny Villuendas-Rey,
  • Ali Bou Nassif,
  • Noé Oswaldo Rodríguez-Rodríguez

摘要

The planning of a software project includes the effort prediction (i.e., the number of person-hours) needed to complete one or more activities of the software development life cycle (SDLC). One of these activities corresponds to implementation. Effort is used for managers for budgeting software projects. Managers commonly take as reference the effort percentage of any SDLC activity with respect to the total SDLC effort. Regarding implementation activity, we found a range between 0.31 and 27.39% for new projects, and between 0.15 and 32.30% for maintained projects, which could cause uncertainty in managers. Therefore, we propose a new nature-inspired algorithm termed Mexican Axolotl Optimization with Random Incorporation (MAORI) as a general metaheuristic for predicting the implementation effort of projects. We embedded statistical regression equations (SRE) in the MAORI to obtain a MAORI-SRE algorithm. Results showed that MAORI-SRE prediction accuracy was better than SRE when optimized through particle swarm optimization, and genetic algorithms.