scholarly journals Comparing Soft Computing Techniques For Early Stage Software Development Effort Estimations

2012 ◽  
Vol 3 (2) ◽  
pp. 119-127 ◽  
Author(s):  
Roheet Bhatnagar
Keyword(s):  
Software Development ◽  
Soft Computing ◽  
Early Stage ◽  
Development Effort ◽  
Soft Computing Techniques ◽  
Software Development Effort

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 Soft Computing and Decision Support System for Software Process Improvement: A Systematic Literature Review

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
He Xiaolong ◽  
Zhao Huiqi ◽  
Zhong Lunchao ◽  
Shah Nazir ◽  
Deng Jun ◽  
...  
Keyword(s):  
Literature Review ◽  
Software Development ◽  
Soft Computing ◽  
Systematic Literature Review ◽  
High Performance ◽  
Software Process Improvement ◽  
Assessment Process ◽  
Development Effort ◽  
Software Project ◽  
Software Development Effort

Software project development is very crucial, and measuring the exact cost and effort of development is becoming tedious and challenging. Organizations are trying to wind up their project of software development within the agreed budget and schedule successfully. Traditional practices are inadequate to achieve the current needs of the software industry. Underestimation and overestimation of software development effort lead to financial implications in the form of resources, cost of staffing, and budget of developing the software project. Soft computing (SC) approaches and tools deliver an addition of techniques for anticipating resistance to the deception, defect, incomplete truth for traceability and ambiguity, low arrangement cost, and strength. A large amount of SC approaches is prevailing in the literature to accomplish way-out to difficulties precisely, practically, and speedily. The approaches of SC can give better prediction, high performance, and dynamic behavior. SC deals with computational intelligence which integrates the concept of agent paradigm and SC. The proposed study presents a systematic literature review (SLR) of the approaches, tools, and techniques of SC used in the literature. The study presented a comprehensive review by searching the defined keywords in the popular libraries, filtered the paper, and obtained most relevant papers. After the selection of the papers, the quality assessment process of the included papers has been done in order to determine the relevancy of the papers. The study will help researchers in the area of research to devise novel ideas and solutions to overcome the existing issue on the basis of this study as evidence of the literature.

Software Cost Estimation using Soft Computing Approaches

2010 ◽  
pp. 499-518 ◽  
Author(s):  
K. Vinaykumar ◽  
V. Ravi ◽  
Mahil Carr
Keyword(s):  
Software Development ◽  
Soft Computing ◽  
Cost Estimation ◽  
Radial Basis Function Network ◽  
Development Effort ◽  
Software Cost ◽  
Ensemble Models ◽  
Software Products ◽  
Software Development Effort ◽  
The Cost

Software development has become an essential investment for many organizations. Software engineering practitioners have become more and more concerned about accurately predicting the cost of software products to be developed. Accurate estimates are desired but no model has proved to be successful at effectively and consistently predicting software development cost. This chapter investigates the use of the soft computing approaches in predicting the software development effort. Various statistical and intelligent techniques are employed to estimate software development effort. Further, based on the abovementioned techniques, ensemble models are developed to forecast software development effort. Two types of ensemble models viz., linear (average) and nonlinear are designed and tested on COCOMO’81 dataset. Based on the experiments performed on the COCOMO’81 data, it was observed that the nonlinear ensemble using radial basis function network as arbitrator outperformed all the other ensembles and also the constituent statistical and intelligent techniques. The authors conclude that using soft computing models they can accurately estimate software development effort.

scholarly journals A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques

2019 ◽  
Vol 24 (2) ◽  
pp. 82-93
Author(s):  
Mahdi Khazaiepoor ◽  
Amid Khatibi Bardsiri ◽  
Farshid Keynia
Keyword(s):  
Data Collection ◽  
Software Development ◽  
High Efficiency ◽  
Development Effort ◽  
Effort Estimation ◽  
Proposed Model ◽  
Soft Computing Techniques ◽  
Software Development Effort ◽  
Data Collections ◽  
Software Development Effort Estimation

Abstract During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the preliminary development stages. These studies have resulted in the offering of a great many of the models. Despite the large deal of efforts, the substantial problems of the offered methods are their dependency on the used data collection and, sometimes, their lack of appropriate efficiency. The current article attempts to present a model for software development effort estimation through making use of evolutionary algorithms and neural networks. The distinctive characteristic of this model is its lack of dependency on the collection of data used as well as its high efficiency. To evaluate the proposed model, six different data collections have been used in the area of software effort estimation. The reason for the application of several data collections is related to the investigation of the model performance independence of the data collection used. The evaluation scales have been MMRE, MdMRE and PRED (0.25). The results have indicated that the proposed model, besides delivering high efficiency in contrast to its counterparts, produces the best responses for all of the used data collections.

Sign in / Sign up

Export Citation Format

Share Document