scholarly journals A Pragmatic Cost Effective Model to Enhance the COCOMO Model in Software Estimation

2020 ◽  
Vol 9 (5) ◽  
pp. 1745-1748
Keyword(s):  
Genetic Algorithm ◽  
Cost Estimation ◽  
Research Work ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Beneficial Result ◽  
Cocomo Model ◽  
The Cost

This research work is aimed at to provide effective cost estimation methodology emphasize on cost effort and time . This paper summarizes the cost effort estimation of most conventionally used models like organic and semi-detached models using an improved version of genetic algorithm that enhances an empirical methodology to reduce the cost factor and time factor in software projects. Constructive cost model(Cocomo model) is broadly used for the fruitful valuation of cost estimation which is based on KLOC method(thousands of lines of code).This method yields beneficial result in case of lines of code method but lacks in terms of concept and logics. The same is estimated directly and is computed using the function point analysis. In the software development lifecycle, the software cost effort estimation is the most demanding process. The accuracy of the estimate in choosing the estimation model is an essential factor. Such conventional software effort estimation techniques fail to compute the accuracy of effort estimation and it is not up to the mark. So here, we tend to propose the cost reduction in the software projects by using the improved version of the known genetic algorithm.

Insights on Cost Estimation Methods and its Uses in Software Project Design

2020 ◽  
pp. 166-169
Author(s):  
Aravindhan K
Keyword(s):  
Project Management ◽  
Cost Estimation ◽  
Estimation Methods ◽  
Software Project ◽  
Estimation Model ◽  
Software Projects ◽  
Software Applications ◽  
Software Companies ◽  
Cost Estimation Model ◽  
The Cost

Cost estimation of software projects is risky task in project management field. It is a process of predicting the cost and effort required to develop a software applications. Several cost estimation models have been proposed over the last thirty to forty years. Many software companies track and analyse the current project by measuring the planed cost and estimate the accuracy. If the estimation is not proper then it leads to the failure of the project. One of the challenging tasks in project management is how to evaluate the different cost estimation and selecting the proper model for the current project. This paper summarizes the different cost estimation model and its techniques. It also provides the proper model selection for the different types of the projects.

scholarly journals Cost Estimate Model for Software Projects using GREYWOLF Algorithm and COCOMO Model

2019 ◽  
Vol 8 (4) ◽  
pp. 7824-7828
Keyword(s):  
Cost Estimation ◽  
Project Manager ◽  
Cost Estimate ◽  
Software System ◽  
Actual Cost ◽  
Software Projects ◽  
Cost Drivers ◽  
Cocomo Model ◽  
The Cost ◽  
System Project

Cost estimation analysis for the software system project is the foremost difficult tasks in software organizations. In this paper, a comparison between an estimate and actual effort was done by applying the grey wolf’s algorithm to predict the effort and time of this software system for a given archive. The intermediate semi-detached COCOMO model was used with the grey wolf’s algorithm by taking the KLOC of the dataset as input, additionally to fifteen cost drivers and giving effort and time as output. The recommended model of the cost estimation helps the project manager by offering a fast and truly estimates the hassle and time of software system project which is nearer to the actual cost.

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.

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.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
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.

scholarly journals Fuzzy Optimization Algorithm for Software Cost Estimation

2020 ◽  
Vol 9 (3) ◽  
pp. 1549-1554
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Cost Estimation ◽  
Fuzzy Optimization ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Estimation Model ◽  
Software Cost ◽  
Clear Cut ◽  
Linguistic Quantifiers

Estimation of a software cost depends on a probabilistic model and thus it doesn't create precise values. In any case, accessibility of good chronicled information combined with a efficient technique can create improved outcomes. This paper, we have displayed a Software Effort Estimation Model utilizing PSO and Fuzzy Logic. Fuzzy sets have been utilized for displaying uncertainty and imprecision in estimation of effort while PSO has been utilized for tuning parameters. This has been seen from the outcomes that Fuzzy-PSO intelligence gives precise outcomes when compared through its different partners. This system relies upon thinking by linguistic quantifiers and fuzzy logic. This kind of model holds well, when the product plans are communicated by absolute or potentially arithmetical data. Along these lines, this projected methodology improves the old style correlation process that doesn't think about clear cut data. In the fuzzy correlation model, fuzzy sets are used to describe both the clear cut and the arithmetical data.

Genetic Algorithm and Support Vector Regression for Software Effort Estimation

2011 ◽  
Vol 282-283 ◽  
pp. 748-752 ◽  
Author(s):  
Jin Cherng Lin ◽  
Chu Ting Chang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Developers ◽  
Factors Affecting ◽  
Proposed Model ◽  
Vector Machines ◽  
Best Parameter ◽  
The Cost

For software developers, accurately forecasting software effort is very important. In the field of software engineering, it is also a very challenging topic. Miscalculated software effort in the early phase might cause a serious consequence. It not only effects the schedule, but also increases the cost price. It might cause a huge deficit. Because all of the different software development team has it is own way to calculate the software effort, the factors affecting project development are also varies. In order to solve these problems, this paper proposes a model which combines genetic algorithm (GA) with support vector machines (SVM). We can find the best parameter of SVM regression by the proposed model, and make more accurate prediction. During the research, we test and verify our model by using the historical data in COCOMO. We will show the results by prediction level (PRED) and mean magnitude of relative error (MMRE).

scholarly journals Improved Effort and Cost Estimation Model Using Artificial Neural Networks and Taguchi Method with Different Activation Functions

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 854
Author(s):  
Nevena Rankovic ◽  
Dragica Rankovic ◽  
Mirjana Ivanovic ◽  
Ljubomir Lazic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cost Estimation ◽  
Time Estimation ◽  
Effort Estimation ◽  
Activation Functions ◽  
Estimation Model ◽  
Wide Range ◽  
Software Product ◽  
Artificial Neural

Software estimation involves meeting a huge number of different requirements, such as resource allocation, cost estimation, effort estimation, time estimation, and the changing demands of software product customers. Numerous estimation models try to solve these problems. In our experiment, a clustering method of input values to mitigate the heterogeneous nature of selected projects was used. Additionally, homogeneity of the data was achieved with the fuzzification method, and we proposed two different activation functions inside a hidden layer, during the construction of artificial neural networks (ANNs). In this research, we present an experiment that uses two different architectures of ANNs, based on Taguchi’s orthogonal vector plans, to satisfy the set conditions, with additional methods and criteria for validation of the proposed model, in this approach. The aim of this paper is the comparative analysis of the obtained results of mean magnitude relative error (MMRE) values. At the same time, our goal is also to find a relatively simple architecture that minimizes the error value while covering a wide range of different software projects. For this purpose, six different datasets are divided into four chosen clusters. The obtained results show that the estimation of diverse projects by dividing them into clusters can contribute to an efficient, reliable, and accurate software product assessment. The contribution of this paper is in the discovered solution that enables the execution of a small number of iterations, which reduces the execution time and achieves the minimum error.

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