Proposing a New Software Cost Estimation Model Based on Artificial Neural Networks

Software cost estimation is an essential and important endeavor for the effective implementation of applications development project concerning its price & time plus its direction concerning its monitoring of autonomous applications development jobs. Software cost estimation is the prediction of software development endeavor and applications development time necessary to create a software job. The scheduling is of scheduling Resources, Budget, Time and several equally Precise software cost estimation is regarded as a tricky job as the information concerning the application project to be designed in the time of its beginning and completion remains obscure, thus drives the investigators from both professors and business to research in the exact same. What's more, it's always preferable for any approximation version to be inclusive because precision in estimation versions mutually lies together using their inclusiveness. So software cost estimation procedure being predictive in character hence requires for inclusiveness that will consequently bring inside that the precision. Within this paper, we'll present many versions for software cost estimation according to variants from Artificial Neural Networks which were completed within the research study. One of those models relies on exact choice of drivers as input into an Artificial Neural Network. And others derive from hybrids of Artificial Neural Networks with distinct Meta-heuristic algorithms as utilization of meta-heuristics in forecast issues such as that of program cost estimation is becoming more popularity. Everyone these versions have been experimented with variety of valid data collections.

Download Full-text

Software cost estimation using artificial neural networks

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan) ◽

10.1109/ijcnn.1993.714077 ◽

2005 ◽

Cited By ~ 20

Author(s):

A.R. Venkatachalam

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cost Estimation ◽

Software Cost Estimation ◽

Software Cost ◽

Artificial Neural

Download Full-text

Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽

10.3390/e23070854 ◽

2021 ◽

Vol 23 (7) ◽

pp. 854

Author(s):

Nevena Rankovic ◽

Dragica Rankovic ◽

Mirjana Ivanovic ◽

Ljubomir Lazic

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Cost Estimation ◽

Time Estimation ◽

Effort Estimation ◽

Activation Functions ◽

Estimation Model ◽

Wide Range ◽

Software Product ◽

Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

Download Full-text