Proceedings Article10.1109/ICIT.2006.84
The Soft Computing Approach to Program Development Time Estimation
Vandana Bhattacherjee
- 18 Dec 2006
- pp 291-292
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TL;DR: In this article, a neural network model has been developed to predict the development time of various software programs, which is a three layer feed forward network with 17 neurons in the hidden layer.
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Abstract: Software effort modeling is one of the fields in which machine learning techniques have proved effective. In this paper, attempt has been made to establish a relation between program development time with respect to its dependence on various program and personnel attributes. A neural network model has been developed to predict the development time of various software programs. The model is a three layer feed forward network with 17 neurons in the hidden layer. The model has 5 inputs and 1 output.
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A survey in the area of machine learning and its application for software quality prediction
TL;DR: This paper advocates the use of case-based reasoning (i.e., CBR) to build a software quality prediction system with the help of human experts, and uses different similarity measures to find the best method that increases reliability.
28
Software Quality Estimation using Machine Learning: Case-based Reasoning Technique
TL;DR: This paper advocates the use of case-based reasoning (i.e., CBR) to make a software quality estimation system by the help of human experts by using different similarity measures to find the best method which increases estimation accuracy & reliability.
Complexity metric for analogy based effort estimation
Vandana Bhattacherjee,Prabhat Mahanti,Sanjay Kumar,Saint John +3 more
- 01 Jan 2009
TL;DR: A case based estimation model using complexity metrics is developed and validated it upon student data to estimate the development effort of student programs based on the values of certain attributes using case based reasoning model.
11
The Application of Case-Based Reasoning to Estimation of Software Development Effort
Ekbal Rashid,Vandana Bhattacherjee,Srikanta Patnaik +2 more
- 01 Apr 2012
TL;DR: A case based reasoning model is developed to estimate the development effort of student programs based on the values of certain attributes based upon the use of analogy to achieve accurate project effort estimation.
Enhancing the Accuracy of Case-Based Estimation Model through Early Prediction of Error Patterns
Ekbal Rashid,Srikanta Patnaik,Vandana Bhattacharya +2 more
- 24 Aug 2013
TL;DR: The paper tries to explore the importance of software fault prediction and to minimize them thoroughly with the advanced knowledge of the error-prone modules, so as to enhance the software quality.
6
References
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Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
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A metrics suite for object oriented design
Shyam R. Chidamber,Chris F. Kemerer +1 more
- 02 Sep 2011
TL;DR: This research addresses the needs for software measures in object-orientation design through the development and implementation of a new suite of metrics for OO design, and suggests ways in which managers may use these metrics for process improvement.
Exploring the relationship between design measures and software quality in object-oriented systems
TL;DR: It is shown that by using a subset of measures, accurate models can be built to predict which classes most of the faults are likely to lie in, and the best model shows a percentage of correct classifications higher than 80% and finds more than 90% of faulty classes.
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Modeling development effort in object-oriented systems using design properties
Lionel C. Briand,Jürgen Wüst +1 more
TL;DR: Results indicate that fairly accurate predictions of class effort can be made based on simple measures of the class interface size alone (mean MREs below 30 percent), and the use of hybrid models combining Poisson regression and CART regression trees clearly improves the accuracy of the models as compared to using Poisson regressors alone.
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