A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the dev...A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the development effort are divided into groups based on the distribution of their values in the available dataset. The linguistic terms are identified for the divided groups using fuzzy functions, and the parameters are fuzzified. The fuzzified parameters then adopt associative classification for generating association rules. The association rules depict the parameters influencing the software development effort. As the number of parameters that influence the effort is more, a large number of rules get generated and can reduce the complexity, the generated rules are filtered with respect to the metrics, support and confidence, which measures the strength of the rule. Genetic algorithm is then employed for selecting set of rules with high quality to improve the accuracy of the model. The datasets such as Nasa93, Cocomo81, Desharnais, Maxwell, and Finnish-v2 are used for evaluating the proposed model, and various evaluation metrics such as Mean Magnitude of Relative Error, Mean Absolute Residuals, Shepperd and MacDonell’s Standardized Accuracy, Enhanced Standardized Accuracy and Effect Size are adopted to substantiate the effectiveness of the proposed methods. The results infer that the accuracy of the model is influenced by the metrics support, confidence, and the number of association rules considered for effort prediction.展开更多
This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is p...This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is proposed that updates an increasingly accurate approximate version of the original problem with a view to saving computations at the early iterations, when one is typically far from the optimal solution. The proposed algorithm is compared to classical value iteration for a broad set of adaptive parameters and the results suggest that significant computational savings can be obtained, while also ensuring a robust performance with respect to the parameters.展开更多
Traditional project effort estimation utilizes development models that span the entire project life cycle and thus culminates estimation errors. This exploratory research is the first attempt to break each project act...Traditional project effort estimation utilizes development models that span the entire project life cycle and thus culminates estimation errors. This exploratory research is the first attempt to break each project activity down to smaller work elements. There are eight work elements, each of which is being defined and symbolized with visually distinct shape. The purpose is to standardize the operations associated with the development process in the form of a visual symbolic flow map. Hence, developers can streamline their work systematically. Project effort estimation can be determined based on these standard work elements that not only help identify essential cost drivers for estimation, but also reduce latency cost to enhance estimation efficiency. Benefits of the proposed work element scheme are project visibility, better control for immediate pay-off and, in long term management, standardization for software process automation.展开更多
文摘A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the development effort are divided into groups based on the distribution of their values in the available dataset. The linguistic terms are identified for the divided groups using fuzzy functions, and the parameters are fuzzified. The fuzzified parameters then adopt associative classification for generating association rules. The association rules depict the parameters influencing the software development effort. As the number of parameters that influence the effort is more, a large number of rules get generated and can reduce the complexity, the generated rules are filtered with respect to the metrics, support and confidence, which measures the strength of the rule. Genetic algorithm is then employed for selecting set of rules with high quality to improve the accuracy of the model. The datasets such as Nasa93, Cocomo81, Desharnais, Maxwell, and Finnish-v2 are used for evaluating the proposed model, and various evaluation metrics such as Mean Magnitude of Relative Error, Mean Absolute Residuals, Shepperd and MacDonell’s Standardized Accuracy, Enhanced Standardized Accuracy and Effect Size are adopted to substantiate the effectiveness of the proposed methods. The results infer that the accuracy of the model is influenced by the metrics support, confidence, and the number of association rules considered for effort prediction.
文摘This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is proposed that updates an increasingly accurate approximate version of the original problem with a view to saving computations at the early iterations, when one is typically far from the optimal solution. The proposed algorithm is compared to classical value iteration for a broad set of adaptive parameters and the results suggest that significant computational savings can be obtained, while also ensuring a robust performance with respect to the parameters.
文摘Traditional project effort estimation utilizes development models that span the entire project life cycle and thus culminates estimation errors. This exploratory research is the first attempt to break each project activity down to smaller work elements. There are eight work elements, each of which is being defined and symbolized with visually distinct shape. The purpose is to standardize the operations associated with the development process in the form of a visual symbolic flow map. Hence, developers can streamline their work systematically. Project effort estimation can be determined based on these standard work elements that not only help identify essential cost drivers for estimation, but also reduce latency cost to enhance estimation efficiency. Benefits of the proposed work element scheme are project visibility, better control for immediate pay-off and, in long term management, standardization for software process automation.
