What Are the Significant Cost Drivers for COSMIC Functional Size Based Effort Estimation?

Mobile applications affect our everyday activities and have become more and more information centric. Effort estimation for mobile application is an essential factor to consider in the development cycle. Due to feature complexities and size, effort estimation of mobile applications poses a continued challenge for developers. This paper attempts to adapt COSMIC Function Point and Unified Modeling Language (UML) techniques to estimate the size of a given mobile application. The COSMIC concepts capture data movements of the functional processes whereas the UML class analyzes them. We utilize the Use Case Diagrams, sequence diagrams and class diagrams for mapping the Function user requirements for sizing mobile applications. We further present a new size measurement technique; Unadjusted Mobile COSMIC Function points (UMCFP) to get the functional size of mobile application using Mobile Complex Factors as an input. In this study eight mobile applications were analyzed using UMCFP, Function Point Analysis and COSMIC Function Point. The results were compared with the actual size of previous Mobile application projects.

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Impact of Base Functional Component Types on Software Functional Size Based Effort Estimation

Product-Focused Software Process Improvement - Lecture Notes in Computer Science ◽

10.1007/978-3-540-69566-0_9 ◽

2008 ◽

pp. 75-89 ◽

Cited By ~ 16

Author(s):

Luigi Buglione ◽

Cigdem Gencel

Keyword(s):

Effort Estimation ◽

Functional Component ◽

Functional Size

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AN EXPLORATION OF FUNCTIONAL SIZE BASED EFFORT ESTIMATION MODELS

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194011005347 ◽

2011 ◽

Vol 21 (03) ◽

pp. 413-429 ◽

Cited By ~ 1

Author(s):

SECKIN TUNALILAR ◽

ONUR DEMIRORS

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Regression Analysis ◽

Measurement Unit ◽

Effort Estimation ◽

Total Size ◽

Generic Models ◽

Functional Components ◽

Estimation Models ◽

Functional Size

A number of methods have been proposed to build a relationship between effort and size. These models are generally based on regression analysis and a widely accepted model is not yet available. Although in some sizing methods, such as MKII and IFPUG, different multipliers for the base functional components (BFC) exist, their origin and the purpose of their usage are undefined. The COSMIC method does not treat components separately and assigns the same measurement unit to each of them. In this study we used the Artificial Neural Network and regression based methods to create effort estimation models that take the four components of the COSMIC method into consideration. In the research we compared several functional size based effort models in terms of accuracy using a reliable company dataset. These models comprised not only the generic models proposed in the literature or currently in use, but also specific models that we generated using our dataset with a single and multi-variate regression analysis and the ANN method. We also explored the effect of functional similarity (FS) using our specific models. We found that using BFC instead of total size improved effort estimation models and the ANN method is a useful approach to calibrate these components according to the company characteristics.

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Identifying and Accounting for Environmental Costs

Volume 2: Design and Construction; Pipeline Automation and Measurement; Environmental Issues; Rotating Equipment Technology ◽

10.1115/ipc1998-2115 ◽

1998 ◽

Author(s):

Doug Jagger ◽

Dave Korpach

Keyword(s):

Environmental Performance ◽

Oil And Gas ◽

Environmental Cost ◽

Environmental Costs ◽

Environmental Implications ◽

Bottom Line ◽

Significant Cost ◽

Cost Drivers ◽

Cost Implications ◽

The Cost

Protection of the environment has been and will continue to be a major issue facing the pipeline business around the world. Many of the decisions companies make relating to future investments and ongoing operations have environmental implications. These decisions can have significant cost implications that impact the bottom line of oil and gas transportation companies. Most companies do not track their environmental costs rigorously and thus, do not have a good understanding of the magnitude of these costs. Recently, we have undertaken studies to define and identify the major environmental cost drivers in the industry. As part of these studies, we identified some potential measures of environmental performance and actually measured certain aspects of environmental performance in pipeline companies. This paper will provide insights into the major environmental cost drivers in the industry and will define these cost drivers. It will provide some ideas on “what to measure” relating to environmental costs. Implementing an environmental cost management system is not a trivial task. It is difficult to assess how much of the cost associated with a certain investment is related to the environment. This can only be determined on a project by project basis and will also be unique from company to company. Although there is no “cookbook” approach to implementing this system, this paper will provide some guidance for implementing such a system.

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Ontology-Oriented Software Effort Estimation System for E-commerce Applications Based on Extreme Programming and Scrum Methodologies

The Computer Journal ◽

10.1093/comjnl/bxy141 ◽

2019 ◽

Vol 62 (11) ◽

pp. 1605-1624 ◽

Cited By ~ 2

Author(s):

Muhammad Adnan ◽

Muhammad Afzal ◽

Khadim Hussain Asif

Keyword(s):

Description Logic ◽

Extreme Programming ◽

Effort Estimation ◽

Software Effort Estimation ◽

Software Projects ◽

Measurement Methodology ◽

Estimation System ◽

Comparative Results ◽

Project History ◽

Functional Size

Abstract Presently, software industry is severely suffering from inaccurate effort estimation and inadequate unstructured or semi-structured project history management. In fact, both are difficult to accomplish and hence badly impact the software projects. We proposed improvements in the effort estimation and the project history management of e-commerce projects focusing on Extreme Programing (XP) and Scrum methodologies using ontology models in our software effort estimation system. Proposed system infers suitable estimate in the form of time, resources and lessons learnt as per the project leader’s requirements by using description logic and HermiT reasoner. To validate our approach, we have performed a case study comprising 20 Business-to-Consumer (B2C) web projects and performed comparative analysis on the collected efforts in both XP and Scrum contexts by applying (Mean Magnitude of Relative Error) MMRE and PRED(25) prediction accuracy measures. Likewise, software functional size of understudy e-commerce projects was measured using COSMIC functional size measurement methodology. Regression analysis of relations among actual COSMIC function points, estimated effort, and actual effort spent for the projects show better significance-F and R2 values for our approach. The comparative results show that overall proposed approach provides accurate estimates and significantly improves over planning poker and delphi methods by 10% and 30%, respectively.

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Tuning of Cost Drivers by Significance Occurrences and Their Calibration with Novel Software Effort Estimation Method

Advances in Software Engineering ◽

10.1155/2013/351913 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Brajesh Kumar Singh ◽

Shailesh Tiwari ◽

K. K. Mishra ◽

A. K. Misra

Keyword(s):

Estimation Method ◽

Project Work ◽

Estimation Accuracy ◽

Effort Estimation ◽

Software Effort Estimation ◽

Software Projects ◽

Cost Drivers ◽

Binary Genetic Algorithm ◽

The Cost ◽

Engineering Projects

Estimation is an important part of software engineering projects, and the ability to produce accurate effort estimates has an impact on key economic processes, including budgeting and bid proposals and deciding the execution boundaries of the project. Work in this paper explores the interrelationship among different dimensions of software projects, namely, project size, effort, and effort influencing factors. The study aims at providing better effort estimate on the parameters of modified COCOMO along with the detailed use of binary genetic algorithm as a novel optimization algorithm. Significance of 15 cost drivers can be shown by their impact on MMRE of efforts on original 63 NASA datasets. Proposed method is producing tuned values of the cost drivers, which are effective enough to improve the productivity of the projects. Prediction at different levels of MRE for each project reflects the percentage of projects with desired accuracy. Furthermore, this model is validated on two different datasets which represents better estimation accuracy as compared to the COCOMO 81 based NASA 63 and NASA 93 datasets.

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