F-ONTOCOM: A Fuzzified Cost Estimation Approach for Ontology Engineering

2021 ◽  
Author(s):  
Sonika Malik ◽  
Sarika Jain
Keyword(s):  
Cost Estimation ◽  
Evaluation Criteria ◽  
Ontology Engineering ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Wide Scale ◽  
Ontology Building ◽  
The Cost ◽  
Essential Prerequisite ◽  
Estimation Models

Estimating effort is an essential prerequisite for the wide-scale dispersal of ontologies. Not much attention has yet been paid to this essential aspect of ontology building. To date, ONTOCOM is the most prominent model for ontology cost estimation. Many factors influencing the building cost of an ontology are depicted by linguistic terms like Very High, High, . . . and so on; making them vague and indistinct. This fuzziness is quite uncertain and must be taken into consideration. The available effort estimation models do not consider the uncertainty of fuzziness. In this work, we propose an effort estimation methodology for ontology engineering using Fuzzy Logic i.e. F-ONTOCOM (Fuzzy-ONTOCOM) to overcome of uncertainty and imprecision. We have defined the corresponding Fuzzy sets for each effort multiplier and its associated linguistic value, and represented the same by triangular membership functions. F-ONTOCOM is applied to a dataset of 148 ontology projects and evaluated over various evaluation criteria. FONTOCOM outperforms the existing effort-estimation models; it has been concluded that F-ONTOCOM improves the cost estimation accuracy and estimated cost is very close to actual cost.

Estimating Activity Costs: How the Provision of Accurate Historical Activity Data from a Biased Cost System Can Improve Individuals' Cost Estimation Accuracy

2007 ◽  
Vol 19 (1) ◽  
pp. 133-159 ◽  
Author(s):  
Dan L. Heitger
Keyword(s):  
Cost Estimation ◽  
Estimation Accuracy ◽  
Activity Data ◽  
Research Attention ◽  
Cost System ◽  
Initial Cost ◽  
Activity Cost ◽  
Historical Activity ◽  
The Cost ◽  
Activity Costs

An integral component of effective cost control and performance evaluation is the ability to accurately estimate relationships between activities and overhead costs (i.e., activity costs). Individuals using a single cost pool system often have to rely on memory of historical activity data when estimating activity costs. If individuals' recall of data is representative of the historical data, then reliance on memory should not be detrimental to cost estimation accuracy. However, individuals often possess incorrect initial beliefs about activity costs. These incorrect beliefs are expected to serve as an anchor from which individuals make insufficient adjustments when estimating activity costs based on memory of historical activity data. Multiple cost pool systems frequently provide biased standard rates; however, such systems also provide accurate historical activity data when individuals estimate costs. I extend prior accounting research by experimentally examining whether a multiple cost pool system's provision of accurate historical activity data improves activity cost estimation for individuals with incorrect cost beliefs even when the cost system also provides biased standard rates. The main contribution of the study is its finding that the multiple cost pool system's provision of historical activity data improves individuals' adjustments from their incorrect initial cost beliefs when estimating activity costs, thereby increasing their estimation accuracy. The results suggest that this improved adjustment from incorrect initial cost beliefs occurs because the provision of historical activity data improves individuals' recognition of how wrong their initial cost beliefs were in reality. This result is achieved even though the cost system provides biased standard rates. The ability of flawed cost systems to improve individuals' activity cost estimation in other such ways has received little research attention and is important because of its potential for improving managerial decision making.

Key indexes choosing approach of complex equipment’s development cost based on grey incidence cluster model

2016 ◽  
Vol 6 (1) ◽  
pp. 110-123 ◽  
Author(s):  
Naiming Xie ◽  
Chuanzhen Hu ◽  
Songming Yin
Keyword(s):  
Cost Estimation ◽  
Grey System Theory ◽  
Critical Parameters ◽  
Estimation Accuracy ◽  
Content Type ◽  
Complex Products ◽  
Civil Aircraft ◽  
Complex Equipment ◽  
Key Indicators ◽  
The Cost

Purpose – The purpose of this paper is to establish a combined model for selecting key indexes of complex equipment, and then improve the cost forecasting precision of the model. The problem how to choose the key elements of complex products has always been concerned on many fields, such as cost assessment, investment decision making, etc. Design/methodology/approach – Using Grey System Theory to establish a cost estimation model of complicated equipment is more reasonable under the few data and poor information. Therefore, this paper constructs cost index’s system of complex equipment, and then quantitative and qualitative analysis methods are utilized to calculate the grey entropy between the characteristic parameter and the behavior parameters. Further, establish the grey relational clustering matrix of the behavior sequences by using the grey relative incidence analysis. Finally, the authors select key indicators according to the grey degree. Findings – The experiment demonstrates that the cost key parameters of complex equipment can be successfully screened out by the proposed approach, and the cost estimation accuracy of complicated products is improved. Practical implications – The method proposed in this paper could be utilized to solve some practical problems, particularly the selection of cost critical parameters for complex products with few samples and poor information. Taking the cost key indexes of civil aircraft as an example, the results verified the validity of the GICM model. Originality/value – In this paper, the authors develop the method of GICM model. Taking the data of civil aircraft as an example, the authors screen the key indicators of complex products successfully, and improve the prediction accuracy of the GM (1, N) model by using the selected parameters, which provides a reference for some firms.

Cost Estimation and Security Investment of Security Projects

2018 ◽  
pp. 4849-4861
Author(s):  
Yosra Miaoui ◽  
Boutheina A. Fessi ◽  
Noureddine Boudriga
Keyword(s):  
Cost Estimation ◽  
Data Privacy ◽  
Financial Constraints ◽  
Future Directions ◽  
Security Investment ◽  
Factors Affecting ◽  
Investment Allocation ◽  
The Cost ◽  
Investment Models ◽  
Estimation Models

This chapter aims at examining two main aspects in security project: cost estimation and investment assessment. The characteristics of security projects are stressed on and the importance of adopting management task is determined. In addition, the chapter examines the different cost estimation models developed for security project and discusses the technical and managerial factors affecting the cost estimation and the management of project. In addition, a review of research works directed toward security investment models is determined. In fact, most models have focused on determining the optimal security investment allocation based on budgetary aspect, economic, and financial constraints. Recent models are interested to examine more specific security features when assessing the required investment (e.g. system vulnerabilities, attacks type, risk factors, data privacy, and insurance). finally, the chapter discusses future directions that could be investigated to make available useful models for cost estimation and investment on security projects.

Effort Estimation Model for each Phase of Software Development Life Cycle

2012 ◽  
pp. 238-246
Author(s):  
Sarah Afzal Safavi ◽  
Maqbool Uddin Shaikh
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Cost Estimation ◽  
Use Case ◽  
Effort Estimation ◽  
Cost Overrun ◽  
Estimation Model ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
The Cost

The assessment of main risks in software development discloses that a major threat of delays are caused by poor effort / cost estimation of the project. Low / poor cost estimation is the second highest priority risk [Basit Shahzad]. This risk can affect four out of a total five phases of the software development life cycle i.e. Analysis, Design, Coding and Testing. Hence targeting this risk alone may reduce the overall risk impact of the project by fifty percent. Architectural designing of the system is a great activity which consumes most of the time in SDLC. Obviously, effort is put forth to produce the design of the system. It is evident that none of the existing estimation models try to calculate the effort put on designing of the system. Although use case estimation model uses the use case points to estimate the cost. But what is the cost of creating use cases? One reason of poor estimates produced by existing models can be negligence of design effort/cost. Therefore it shall be well estimated to prevent any cost overrun of the project. We propose a model to estimate the effort in each of these phases rather than just relying upon the cost estimation of the coding phase only. It will also ease the monitoring of project status and comparison against planned cost and actual cost incurred so far at any point of time.

Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

2022 ◽  
pp. 1652-1665
Author(s):  
Kazunori Iwata ◽  
Toyoshiro Nakashima ◽  
Yoshiyuki Anan ◽  
Naohiro Ishii
Keyword(s):  
Software Development ◽  
Embedded Software ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Development Projects ◽  
Effort Estimation ◽  
Software Projects ◽  
Machine Learning Classification ◽  
Artificial Neural Network Ann ◽  
Estimation Models

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

scholarly journals Improving Effort Estimation by Voting Software Estimation Models

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Luiz Fernando Capretz ◽  
Venus Marza
Keyword(s):  
Estimation Accuracy ◽  
Data Repository ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Projects ◽  
Software Estimation ◽  
Software Development Effort ◽  
Commercial Purpose ◽  
Estimation Models

Estimating software development effort is an important task in the management of large software projects. The task is challenging, and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel models to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. Empirical validation of this approach uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement in software estimation accuracy.

scholarly journals Improving the cost estimates of complex projects in the project-based industries

2017 ◽  
Vol 7 (2) ◽  
pp. 173-184 ◽  
Author(s):  
Pournima Sridarran ◽  
Kaushal Keraminiyage ◽  
Leon Herszon
Keyword(s):  
Best Practices ◽  
Cost Estimation ◽  
Estimation Accuracy ◽  
Effective Practices ◽  
Accurate Estimation ◽  
Content Type ◽  
Complex Projects ◽  
Cost Implications ◽  
The Cost ◽  
Improve Cost

Purpose Project-based industries face major challenges in controlling project cost and completing within the budget. This is a critical issue as it often connects to the main objectives of any project. However, accurate estimation at the beginning of the project is difficult. Scholars argue that project complexity is a major contributor to cost estimation inaccuracies. Therefore, recognising the priorities of acknowledging complexity dimensions in cost estimation across similar industries is beneficial in identifying effective practices to reduce cost implications. Hence, the purpose of this paper is to identify the level of importance given to different complexity dimensions in cost estimation and to recognise best practices to improve cost estimation accuracy. Design/methodology/approach An online questionnaire survey was conducted among professionals including estimators, project managers, and quantity surveyors to rank the identified complexity dimensions based on their impacts in cost estimation accuracy. Besides, in-depth interviews were conducted among experts and practitioners from different industries, in order to extract effective practices to improve the cost estimation process of complex projects. Findings Study results show that risk, project and product size, and time frame are the high-impact complexity dimensions on cost estimation, which need more attention in reducing unforeseen cost implications. Moreover, study suggests that implementing a knowledge sharing system will be beneficial to acquire reliable and adequate information for cost estimation. Further, appropriate staffing, network enhancement, risk management, and circumspect estimation are some of the suggestions to improve cost estimation of complex projects. Originality/value The study finally provides suggestions to improve cost estimation in complex projects. Further, the results are expected to be beneficial to learn lessons from different industries and to exchange best practices.

scholarly journals Enhancing Delphi Method with Algorithmic Estimates for Software Effort Estimation: An Experimental Study

2021 ◽  
Vol 12 (04) ◽  
pp. 01-18
Author(s):  
Tharwon Arnuphaptrairong
Keyword(s):  
Delphi Method ◽  
Cost Estimation ◽  
Expert Judgment ◽  
Delphi Process ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Delphi Experiment ◽  
Individual Expert

Literature review shows that more accurate software effort and cost estimation methods are needed for software project management success. Expert judgment and algorithmic model estimation are two predominant methods discussed in the literature. Both are reported almost at the comparable level of accuracy performance. The combination of the two methods is suggested to increase the estimation accuracy. Delphi method is an encouraging structured expert judgment method for software effort group estimation but surprisingly little was reported in the literature. The objective of this study is to test if the Delphi estimates will be more accurate if the participants in the Delphi process are exposed to the algorithmic estimates. A Delphi experiment where the participants in the Delphi process were exposed to three algorithmic estimates –Function Points, COCOMO estimates, and Use Case Points, was therefore conducted. The findings show that the Delphi estimates are slightly more accurate than the statistical combination of individual expert estimates, but they are not statistically significant. However, the Delphi estimates are statistically significant more accurate than the individual estimates. The results also show that the Delphi estimates are slightly less optimistic than the statistical combination of individual expert estimates but they are not statistically significant either. The adapted Delphi experiment shows a promising technique for improving the software cost estimation accuracy.

Effort Estimation Model for each Phase of Software Development Life Cycle

2011 ◽  
pp. 270-277
Author(s):  
Sarah Afzal Safavi ◽  
Maqbool Uddin Shaikh
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Cost Estimation ◽  
Use Case ◽  
Effort Estimation ◽  
Cost Overrun ◽  
Estimation Model ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
The Cost

The assessment of main risks in software development discloses that a major threat of delays are caused by poor effort / cost estimation of the project. Low / poor cost estimation is the second highest priority risk [Basit Shahzad]. This risk can affect four out of a total five phases of the software development life cycle i.e. Analysis, Design, Coding and Testing. Hence targeting this risk alone may reduce the overall risk impact of the project by fifty percent. Architectural designing of the system is a great activity which consumes most of the time in SDLC. Obviously, effort is put forth to produce the design of the system. It is evident that none of the existing estimation models try to calculate the effort put on designing of the system. Although use case estimation model uses the use case points to estimate the cost. But what is the cost of creating use cases? One reason of poor estimates produced by existing models can be negligence of design effort/cost. Therefore it shall be well estimated to prevent any cost overrun of the project. We propose a model to estimate the effort in each of these phases rather than just relying upon the cost estimation of the coding phase only. It will also ease the monitoring of project status and comparison against planned cost and actual cost incurred so far at any point of time.

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