3,739 research outputs found
Software Effort Estimation using Neuro Fuzzy Inference System: Past and Present
Most important reason for project failure is poor effort estimation. Software development effort estimation is needed for assigning appropriate team members for development, allocating resources for software development, binding etc. Inaccurate software estimation may lead to delay in project, over-budget or cancellation of the project. But the effort estimation models are not very efficient. In this paper, we are analyzing the new approach for estimation i.e. Neuro Fuzzy Inference System (NFIS). It is a mixture model that consolidates the components of artificial neural network with fuzzy logic for giving a better estimation
Software development effort estimation modeling using a combination of fuzzy-neural network and differential evolution algorithm
Software cost estimation has always been a serious challenge lying ahead of software teams that should be seriously considered in the early stages of a project. Lack of sufficient information on final requirements, as well as the existence of inaccurate and vague requirements, are among the main reasons for unreliable estimations in this area. Though several effort estimation models have been proposed over the recent decade, an increase in their accuracy has always been a controversial issue, and researchers' efforts in this area are still ongoing. This study presents a new model based on a hybrid of adaptive network-based fuzzy inference system (ANFIS) and differential evolution (DE) algorithm. This model tries to obtain a more accurate estimation of software development effort that is capable of presenting a better estimate within a wide range of software projects compared to previous works. The proposed method outperformed other optimization algorithms adopted from the genetic algorithm, evolutionary algorithms, meta-heuristic algorithms, and neuro-fuzzy based optimization algorithms, and could improve the accuracy using MMRE and PRED (0.25) criteria up to 7%
Optimizing Effort and Time Parameters of COCOMO II Estimation using Fuzzy Multi-objective PSO
The estimation of software effort is an essential and crucial activity for the software development life cycle. Software effort estimation is a challenge that often appears on the project of making a software. A poor estimate will produce result in a worse project management. Various software cost estimation model has been introduced to resolve this problem. Constructive Cost Model II (COCOMO II Model) create large extent most considerable and broadly used as model for cost estimation. To estimate the effort and the development time of a software project, COCOMO II model uses cost drivers, scale factors and line of code. However, the model is still lacking in terms of accuracy both in effort and development time estimation. In this study, we do investigate the influence of components and attributes to achieve new better accuracy improvement on COCOMO II model. And we introduced the use of Gaussian Membership Function (GMF) Fuzzy Logic and Multi-Objective Particle Swarm Optimization method (MOPSO) algorithms in calibrating and optimizing the COCOMO II model parameters. The proposed method is applied on Nasa93 dataset. The experiment result of proposed method able to reduce error down to 11.891% and 8.082% from the perspective of COCOMO II model. The method has achieved better results than those of previous researches and deals proficient with inexplicit data input and further improve reliability of the estimation method
Optimizing complexity weight parameter of use case points estimation using particle swarm optimization
Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant
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