Risk Analysis in Software Cost Estimation: A Simulation-Based Approach

Risk analysis and cost estimation are two important aspects of project planning that can either make the way or break the way to a project’s success. At the same, both these tasks are difficult and painstaking, but whether someone likes it and not, the project’s success depends heavily on them. As documented by Fredric Brooks Junior in his legendry book “The Mythical Man-Month,” planning, scheduling, and estimation have been central to software engineering since its early days in the 1970s. Present communication presents a simulation-based approach to estimate the costing schedule of a software development project. The results show that simulation is expedient as well as efficient in terms of time, effort, and cost requirement and provides pragmatic results.

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Predicting Project Effort Intelligently in early Stages by Applying Genetic Algorithms with Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.2035 ◽

2014 ◽

Vol 513-517 ◽

pp. 2035-2040 ◽

Cited By ~ 1

Author(s):

Zhen You Li

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Cost Estimation ◽

Back Propagation ◽

Development Project ◽

Software Cost ◽

Reduced Models ◽

Software Development Project ◽

Early Stages ◽

Global Optimal

In the early stages of a software development project, estimating the amount of effort is one of the most important project management concerns. This study has successfully produced global optimal reduced models intelligently predicting software cost estimation by employing neural networks with back-propagation learning algorithms combined with genetic algorithms (GA-NN) to determine the most significant explanatory variables among the 16 COCOMO cost drivers. The performance of the full model of GA-NN is much superior to that of the COCOMO, whilst the predicting performance of its global optimal reduced model is also comparable to that of the COCOMO in terms of MMRE and PRED (25). The optimal reduced models and their found significant factors can offer powerful supports for the project managers to make right decisions in the early stages of the projects.

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Functional Link Artificial Neural Networks for Software Cost Estimation

International Journal of Applied Evolutionary Computation ◽

10.4018/jaec.2012040104 ◽

2012 ◽

Vol 3 (2) ◽

pp. 62-82 ◽

Cited By ~ 10

Author(s):

B. Tirimula Rao ◽

Satchidananda Dehuri ◽

Rajib Mall

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Cost Estimation ◽

Project Planning ◽

Support Vector ◽

Software Cost Estimation ◽

Functional Link ◽

Software Cost ◽

Artificial Neural ◽

Polynomial Expansions

Software cost estimation is the process of predicting the effort required to develop a software system. Software development projects often overrun their planned effort as defined at preliminary design review. Software cost estimation is important for budgeting, risk analysis, project planning, and software improvement analysis. In this paper, the authors propose a faster functional link artificial neural network (FLANN) based software cost estimation. By means of preprocessing, i.e., optimal reduced datasets (ORD), the authors make the functional link artificial neural network faster. Optimal reduced datasets, which reduce the whole project base into small subsets that consist of only representative projects. The representative projects are given as input to FLANN and tested on eight state-of-the-art polynomial expansions. The proposed methods are validated on five real time datasets. This approach yields accurate results vis-à-vis conventional FLANN, support vector machine regression (SVR), radial basis function (RBF), classification, and regression trees (CART).

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Investigating the Relationship Between SLOC and Logical Database Measures to Improve the Early Estimation of Software Cost

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194019500177 ◽

2019 ◽

Vol 29 (03) ◽

pp. 401-413 ◽

Cited By ~ 1

Author(s):

Gul Tokdemir ◽

Nergiz Ercil Cagiltay

Keyword(s):

Cost Estimation ◽

Primary Source ◽

Database Design ◽

Close Correlation ◽

Project Planning ◽

Estimation Methods ◽

Design Stage ◽

Size Estimation ◽

Software Cost Estimation ◽

Software Cost

Project planning is a critical activity in the software development life cycle. At the early stages of a project, the managers need to estimate required time, effort and cost to plan, track and then to deliver the project successfully. Many studies have attempted to provide methods for precise software cost estimation. The current software cost estimation methods are mainly based on software size estimation and functional system requirements. The main assumption of this study is that, as the primary source of complexity in today’s software is the interaction between the database and the user, database measures may provide inputs allowing current software estimation methods to achieve more accurate results. Accordingly, this study attempts to gain insights from objective measures, collected through the logical database model of software systems, for better prediction of the software’s effort and hence cost through software lines of code (SLOC) measure. For this purpose, more than 2.5 million lines of code developed by four different companies, for 79 different software packages with their related database design measures, are analyzed. The results of this study show that there is a close correlation between the software size and database design measure, namely, the number of tables which can be collected at the logical database design stage. By adapting this result, the current estimation models could be improved significantly.

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