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首页> 外文期刊>International journal of applied evolutionary computation >The Application of Meta-Heuristic Algorithms to Improve the Performance of Software Development Effort Estimation Models
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The Application of Meta-Heuristic Algorithms to Improve the Performance of Software Development Effort Estimation Models

机译：元启发式算法在提高软件开发工作量估计模型性能中的应用

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摘要

One of the major activities in effective and efficient production of software projects is the precise estimation of software development effort. Estimation of the effort in primary steps of software development is one of the most important challenges in managing software projects. Some reasons for these challenges such as: discordant software projects, the complexity of the manufacturing process, special role of human and high level of obscure and unusual features of software projects can be noted. Predicting the necessary efforts to develop software using meta-heuristic optimization algorithms has made significant progressions in this field. These algorithms have the potent to be used in estimation of the effort of the software. The necessity to increase estimation precision urged the authors to survey the efficiency of some meta-heuristic optimization algorithms and their effects on the software projects. To do so, in this paper, they investigated the effect of combining various optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and ant colony algorithm on different models such as COCOMO, estimation based on analogy, machine learning methods and standard estimation models. These models have employed various data sets to evaluate the results such as COCOMO, Desharnais, NASA, Kemerer, CF, DPS, ISBSG and Koten & Gary. The results of this survey can be used by researchers as a primary reference.
机译：有效有效地生产软件项目的主要活动之一是对软件开发工作的精确估算。评估软件开发主要步骤中的工作量是管理软件项目中最重要的挑战之一。这些挑战的一些原因包括：软件项目不一致，制造过程的复杂性，人的特殊作用以及软件项目的高度模糊和不寻常的功能。预测使用元启发式优化算法开发软件的必要努力在该领域取得了重大进展。这些算法具有可用于估算软件工作量的功能。提高估计精度的必要性促使作者调查一些元启发式优化算法的效率及其对软件项目的影响。为此，本文研究了将遗传算法，粒子群优化算法和蚁群算法等各种优化算法组合到不同模型（例如COCOMO），基于类比的估计，机器学习方法和标准估计模型上的效果。这些模型采用了各种数据集来评估结果，例如COCOMO，Desharnais，NASA，Kemerer，CF，DPS，ISBSG和Koten＆Gary。研究人员可以将该调查的结果作为主要参考。

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