Role of Soft Computing Techniques in Software Effort Estimation: An Analytical Study

2019 ◽  
pp. 807-831 ◽  
Author(s):  
P. Suresh Kumar ◽  
H. S. Behera
Keyword(s):  
Soft Computing ◽  
Analytical Study ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Soft Computing Techniques

scholarly journals SEEPC: A Toolbox for Software Effort Estimation using Soft Computing Techniques

2011 ◽  
Vol 31 (4) ◽  
pp. 12-19 ◽  
Author(s):  
Hari .CH.V.M.K ◽  
Tegjyot Singh Sethi ◽  
Author Jagadeesh.M
Keyword(s):  
Soft Computing ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Soft Computing Techniques

scholarly journals Comparison of hard limiting and soft computing methods for predicting software effort estimation: In reference to Small Scale Visualization Projects

2018 ◽  
Vol 7 (4.6) ◽  
pp. 294
Author(s):  
T. M Kiran Kumar ◽  
M. A. Jayaram
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Small Scale ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Grade Point ◽  
Error Matrix ◽  
Prototype Development ◽  
Soft Computing Techniques ◽  
Soft Computing Methods

It is a well known fact that software effort estimation is exceptionally critical in every software industry, particular during the development of projects. It is hard to estimate the parameters involved due to ambiguity and uncertainty associated with the parameters. It is exactly here the hard limiting techniques, soft computing techniques comes to play. In this unique circumstance, this paper, presents an attempt to that compare the two paradigms for effort estimation. For this, we have considered fifty real time small visualization projects thrive by post graduate students.  The prototype development involves following stages:         i)            Elicitation of seven novel parameters namely Lines of Code, Cumulative Grade Point Average, New and changed code, Reused code, Cyclomatic Complexity, Algorithmic Complexity and Functional Points.       ii)            Developing of hard limiting methods and soft computing methods for prediction of software effort involved in terms of duration in minutes.For the validation of the models error metrics namely: Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), Mean of Magnitude of error Relative to the Estimate (MMER) and Root Mean Square Error (RMSE) have been used. The result showed that the models compared very well with marginal difference in terms of predict values of error matrix. 

scholarly journals Comparison of hard limiting and soft computing methods for predicting software effort estimation: In reference to Small Scale Visualization Projects

2018 ◽  
Vol 7 (4.6) ◽  
pp. 291 ◽  
Author(s):  
T. M Kiran Kumar ◽  
M. A. Jayaram
Keyword(s):  
Soft Computing ◽  
Computing Methods ◽  
Small Scale ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Grade Point ◽  
Error Matrix ◽  
Prototype Development ◽  
Soft Computing Techniques ◽  
Soft Computing Methods

It is a well known fact that software effort estimation is exceptionally critical in every software industry, particular during the development of projects. It is hard to estimate the parameters involved due to ambiguity and uncertainty associated with the parameters. It is exactly here the hard limiting techniques, soft computing techniques comes to play. In this unique circumstance, this paper, presents an attempt to that compare the two paradigms for effort estimation. For this, we have considered fifty real time small visualization projects thrive by post graduate students.  The prototype development involves following stages:         i)            Elicitation of seven novel parameters namely Lines of Code, Cumulative Grade Point Average, New and changed code, Reused code, Cyclomatic Complexity, Algorithmic Complexity and Functional Points.       ii)            Developing of hard limiting methods and soft computing methods for prediction of software effort involved in terms of duration in minutes.For the validation of the models error metrics namely: Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), Mean of Magnitude of error Relative to the Estimate (MMER) and Root Mean Square Error (RMSE) have been used. The result showed that the models compared very well with marginal difference in terms of predict values of error matrix.  

scholarly journals An Effective Software Effort Estimation based on Functional Points using Soft Computing Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 3729-3733
Keyword(s):  
Software Development ◽  
Soft Computing ◽  
Early Stage ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Fuzzy Techniques

Still in this 21st century, it is a great challenge for the Project Managers to make the software projects successful. The success of software projects relies on how accurately the estimates of effort, cost and duration can be made. Most of the standard surveys stated that only 30-40% of software projects are successful and the remaining are either challenged, cancelled or failed. One of the key reasons for failure of projects is inaccurate estimations. Effort Estimation should be carried out in the early stage of Software Development Life Cycle (SDLC) and it is an essential activity to establish scope & business case of software project management activities. Over estimation or under estimation leads to failure of the software projects. Many of the stakeholders are expecting the estimation of development effort in early stage for their better bidding. There are many methodologies like KLOC, Use Case Points (UCP), Class Points, Story Points, Test Case Points, Functional Points (FP), etc. to estimate effort in the software development. To estimate the effort in the early stage of software development, UCP, Story Points and FP are more preferable. The methods for estimation may be adopted based on the project complexity, functionality, approaches etc. In order to achieve an efficient and reliable effort estimate and thereby have a proper execution of software development plan, Soft Computing Techniques can be adopted in the various organizations and different research domains. In this paper, Functional Points have been selected for effort estimation and implemented using soft computing techniques like Neural Networks and Neuro Fuzzy techniques. After examination the results are evaluated using different error measures like VAF,MMRE,RAE, RRSE and PRED. Basing on results it is observed that the Neuro Fuzzy techniques provided better effort estimates

scholarly journals A soft computing framework for software effort estimation

2005 ◽  
Vol 10 (2) ◽  
pp. 170-177 ◽  
Author(s):  
Xishi Huang ◽  
Danny Ho ◽  
Jing Ren ◽  
Luiz F. Capretz
Keyword(s):  
Soft Computing ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Computing Framework
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