The software stage-effort estimation can be used to dynamically adjust software project schedule, further to help make the project finished on budget. This paper presents a grey model Verhulst based method for stage-e...The software stage-effort estimation can be used to dynamically adjust software project schedule, further to help make the project finished on budget. This paper presents a grey model Verhulst based method for stage-effort estimation during software development process, a bias correction technology was used to improve the estimation accuracy. The proposed method was evaluated with a large-scale industrial software engineering database. The results are very encouraging and indicate the method has considerable potential.展开更多
The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other ob...The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper- based feature selection method, multi-objective feature selection for analogy-based software effort estimation （MASE）, is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE （mean magnitude of relative error）, MdMRE （median magnitude of relative error）, PRED （ 0. 25 ）, and SA （ standardized accuracy） performance metrics.展开更多
In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results i...In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.展开更多
Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates...Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.展开更多
Parametric software effort estimation models usually consists of only a single mathematical relationship. With the advent of software repositories containing data from heterogeneous projects, these types of models suf...Parametric software effort estimation models usually consists of only a single mathematical relationship. With the advent of software repositories containing data from heterogeneous projects, these types of models suffer from poor adjustment and predictive accuracy. One possible way to alleviate this problem is the use of a set of mathematical equations obtained through dividing of the historical project datasets according to different parameters into subdatasets called partitions. In turn, partitions are divided into clusters that serve as a tool for more accurate models. In this paper, we describe the process, tool and results of such approach through a case study using a publicly available repository, ISBSG. Results suggest the adequacy of the technique as an extension of existing single-expression models without making the estimation process much more complex that uses a single estimation model. A tool to support the process is also presented. Keywords software engineering, software measurement, effort estimation, clustering展开更多
基金Supported by the National Natural Science Foundation of China (60673124)the National High Technology Research and Development Program of China (863 Program)(2006AA01Z183).
文摘The software stage-effort estimation can be used to dynamically adjust software project schedule, further to help make the project finished on budget. This paper presents a grey model Verhulst based method for stage-effort estimation during software development process, a bias correction technology was used to improve the estimation accuracy. The proposed method was evaluated with a large-scale industrial software engineering database. The results are very encouraging and indicate the method has considerable potential.
基金The National Natural Science Foundation of China(No.61602267,61202006)the Open Project of State Key Laboratory for Novel Software Technology at Nanjing University(No.KFKT2016B18)
文摘The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper- based feature selection method, multi-objective feature selection for analogy-based software effort estimation （MASE）, is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE （mean magnitude of relative error）, MdMRE （median magnitude of relative error）, PRED （ 0. 25 ）, and SA （ standardized accuracy） performance metrics.
基金This work was supported by the Technology development Program of MSS[No.S3033853].
文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.
文摘Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.
基金This work is supported by the Spanish Ministry of Science and Technology under Grant No.CICYT TIN2004-06689-C03.
文摘Parametric software effort estimation models usually consists of only a single mathematical relationship. With the advent of software repositories containing data from heterogeneous projects, these types of models suffer from poor adjustment and predictive accuracy. One possible way to alleviate this problem is the use of a set of mathematical equations obtained through dividing of the historical project datasets according to different parameters into subdatasets called partitions. In turn, partitions are divided into clusters that serve as a tool for more accurate models. In this paper, we describe the process, tool and results of such approach through a case study using a publicly available repository, ISBSG. Results suggest the adequacy of the technique as an extension of existing single-expression models without making the estimation process much more complex that uses a single estimation model. A tool to support the process is also presented. Keywords software engineering, software measurement, effort estimation, clustering
