Empirical Studies of Software Cost Estimation: Training of Effort Estimation Uncertainty Assessment Skills

Software cost estimation is important for information systems management and is generally taught in software engineering courses especially to terms of the ever-increasing development of E-Government. A significant challenge for Software Sizing (SS) is to determine cost estimates based on TOR documents that do not yet contain complete Software Requirements Specifications (SRS). This study uses Function Points as one of the measurement methods that can make cost estimates and expert needs based on the desired functional system. On the other hand, there are cases where estimation needs after the SRS document have been made. Thus in this study, the authors discuss the implementation of SS based on TOR and SRS documents for E-Government. The results of this study indicate the closeness of the actual and estimated values of 81.9% for TOR and 93.4% for SRS.

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Empirical studies of assumptions that underlie software cost-estimation models

Information and Software Technology ◽

10.1016/0950-5849(92)90077-3 ◽

1992 ◽

Vol 34 (4) ◽

pp. 211-218 ◽

Cited By ~ 78

Author(s):

BA Kitchenham

Keyword(s):

Cost Estimation ◽

Empirical Studies ◽

Software Cost Estimation ◽

Software Cost ◽

Estimation Models ◽

Cost Estimation Models

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Optimization Criteria for Effort Estimation using Fuzzy Technique

CLEI electronic journal ◽

10.19153/cleiej.10.1.2 ◽

2007 ◽

Vol 10 (1) ◽

Cited By ~ 10

Author(s):

Harish Mittal ◽

Pradeep Bhatia

Keyword(s):

Cost Estimation ◽

Triangular Fuzzy Number ◽

Exact Science ◽

Effort Estimation ◽

Software Cost Estimation ◽

Software Projects ◽

Software Cost ◽

Models Comparison ◽

Effective Cost ◽

Single Number

Effective cost estimation is the most challenging activity in software development. Software cost estimation is not an exact science. Cost estimation process involves a series of systematic steps that provide estimate with acceptable risk. Some prevalent LOC based models are- Bailey Basili model, Alaa F. Sheta G.E. Model, and Alaa F. Sheta, Model 2 .Two new models, based on fuzzy logic sizing, are presented in this paper. Rather than using a single number, the software size is regarded as a triangular fuzzy number. We can optimize the estimated effort for any application by varying arbitrary constants for these models. The developed models are tested on 10 NASA software projects, on the basis of four criterions for assessment of software cost estimation models. Comparison of all the models, cited above, is done and it is found that the developed models provide better estimation.

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The Application of Case-Based Reasoning to Estimation of Software Development Effort

International Journal of Computer Science and Informatics ◽

10.47893/ijcsi.2012.1045 ◽

2012 ◽

pp. 239-244

Author(s):

Ekbal Rashid ◽

Vandana Bhattacherjee ◽

Srikanta Patnaik

Keyword(s):

Software Engineering ◽

Cost Estimation ◽

Expert Advice ◽

Case Based Reasoning ◽

Development Effort ◽

Effort Estimation ◽

Software Cost Estimation ◽

Software Cost ◽

Software Development Effort ◽

Case Based

Accurate project effort estimation is an important goal for the software engineering community. Till date most work has focused upon building algorithmic models of effort estimation for example COCOMO. We describe an alternative approach to estimation based upon the use of analogy. The objective is to estimate the development effort of student programs based on the values of certain attributes. We have developed a case based reasoning model and have validated it upon student data. Due to the nature of the software engineering domain, it is important that software cost estimation models should be able to deal with imprecision and uncertainty associated with such values. It is to serve this purpose that we propose our case based model for software cost estimation. We feel that case based models are particularly useful when it is difficult to define concrete rules about a problem domain in addition to this, expert advice may be used to supplement the existing stored knowledge.

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