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Tuning of Cost Drivers by Significance Occurrences and Their Calibration with Novel Software Effort Estimation Method

机译：显着性成本驱动因素的调优及其新型软件工作量估算的标定

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Estimation is an important part of software engineering projects, and the ability to produce accurate effort estimates has an impact on key economic processes, including budgeting and bid proposals and deciding the execution boundaries of the project. Work in this paper explores the interrelationship among different dimensions of software projects, namely, project size, effort, and effort influencing factors. The study aims at providing better effort estimate on the parameters of modified COCOMO along with the detailed use of binary genetic algorithm as a novel optimization algorithm. Significance of 15 cost drivers can be shown by their impact on MMRE of efforts on original 63 NASA datasets. Proposed method is producing tuned values of the cost drivers, which are effective enough to improve the productivity of the projects. Prediction at different levels of MRE for each project reflects the percentage of projects with desired accuracy. Furthermore, this model is validated on two different datasets which represents better estimation accuracy as compared to the COCOMO 81 based NASA 63 and NASA 93 datasets.
机译：估算是软件工程项目的重要组成部分，而产生准确的估算工作量的能力会影响关键的经济流程，包括预算和投标建议以及确定项目的执行范围。本文的工作探讨了软件项目的不同维度之间的相互关系，即项目规模，工作量和工作量影响因素。该研究旨在为改进的COCOMO的参数提供更好的估算效果，以及将二进制遗传算法作为一种新型优化算法的详细使用。通过对原始63个NASA数据集所做的努力对MMRE的影响，可以显示15个成本动因的意义。提议的方法是产生成本动因的调整值，这些值足以提高项目的生产率。每个项目在MRE的不同级别上的预测反映了具有所需准确性的项目百分比。此外，该模型在两个不同的数据集上得到了验证，与基于COCOMO 81的NASA 63和NASA 93数据集相比，该模型具有更好的估计精度。

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