An Ensemble of Hybrid Search-Based Algorithms for Software Effort Prediction

Wasiur Rhmann
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Abstract

Software organizations rely on the estimation of efforts required for the development of software to negotiate customers and plan the schedule of the project. Proper estimation of efforts reduces the chances of project failures. Historical data of projects have been used to predict the effort required for software development. In recent years, various ensemble of machine learning techniques have been used to predict software effort. In the present work, a novel ensemble technique of hybrid search-based algorithms (EHSBA) is used for software effort estimation. Four HSBAs—fuzzy and random sets-based modeling (FRSBM-R), symbolic fuzzy learning based on genetic programming (GFS-GP-R), symbolic fuzzy learning based on genetic programming grammar operators and simulated annealing (GFS_GSP_R), and least mean squares linear regression (LinearLMS_R)—are used to create an ensemble (EHSBA). The EHSBA is compared with machine learning-based ensemble bagging, vote, and stacking on datasets obtained from PROMISE repository. Obtained results reported that EHSBA outperformed all other techniques.
基于混合搜索的软件工作量预测算法集成
软件组织依赖于对软件开发所需工作量的估计来与客户协商并计划项目的进度。适当的工作量评估可以减少项目失败的机会。项目的历史数据已经被用来预测软件开发所需的工作量。近年来,各种机器学习技术的集成已被用于预测软件的工作量。本文提出了一种基于混合搜索算法(EHSBA)的集成技术,用于软件工作量估算。四种hsbas -基于模糊和随机集的建模(FRSBM-R),基于遗传规划的符号模糊学习(GFS-GP-R),基于遗传规划语法算子和模拟退火的符号模糊学习(GFS_GSP_R),以及最小均方线性回归(LinearLMS_R) -用于创建集成(EHSBA)。将EHSBA与基于机器学习的集成装袋、投票和堆叠在PROMISE存储库中获得的数据集上进行了比较。获得的结果报告EHSBA优于所有其他技术。
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