Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO

Langsari, Kholed; Sarno, Riyanarto

doi:10.1109/eecsi.2017.8239157

Cited by 19 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The capacity of a budget estimating model is determined by calculating its bias, stability, and precision. Measures of bias, stability, and precision are concerned with the average difference between actual and the estimated costs, considering both the degree of variation around the average and the combination with bias and consistency [12]. By far, the most popular evaluation criteria used involve statistics such as mean, standard deviation, and coefficient of variation [10].…”

Section: Article Historymentioning

confidence: 99%

“…Effort and schedule are two parameters and outcomes that define the quality of any software product. An effort is the amount of labor required to complete a task, measured in person-month units [12]. Schedule simple means the amount of time required to complete the job, which is proportional to the effort.…”

Section: Cocomo Modelmentioning

confidence: 99%

“…Particles can update their positions and velocities according to environmental change. In addition, the swarm in PSO does not limit its movement but continuously searches for the optimal solution in the possible solution space [12] [28]. Particles in PSO can keep their stable movement in the search space while change their movement mode to adapt to the change in the environment [34].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

See 2 more Smart Citations

Optimized COCOMO parameters using hybrid particle swarm optimization

Zakaria

Ismail

Abidin

et al. 2021

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.

show abstract

Section: Article Historymentioning

confidence: 99%

Section: Cocomo Modelmentioning

confidence: 99%

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

See 1 more Smart Citation

Optimized COCOMO parameters using hybrid particle swarm optimization

Zakaria

Ismail

Abidin

et al. 2021

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

show abstract

“…Langsari and Sarno [27] suggested hybrid models by using the COCOMO II and fuzzy logic to estimate the project cost. Esteki et al [28] suggested the augmentation of the cost control methods with a risk management framework.…”

Section: Related Workmentioning

confidence: 99%

Enhancing software development cost control by forecasting the cost of rework: preliminary study

Khalid

Yeoh

2021

IJEECS

View full text Add to dashboard Cite

<p><span>Industrial reports show massive cost overruns associated with software projects. The cost of software reworks constitutes a large portion of the overall cost, reflecting a substantial challenge in cost control. Earned value management (EVM) is the most recognized model for project cost control. However, it shows many limitations in forecasting the software project cost, leading to a considerable challenge in cost control. Nevertheless, the major EVM limitation found is its inability to forecast the cost of software rework. This research investigated the factors affecting this limitation and suggests an enhanced EVM model. The significant contribution of this research is its incorporation of software-related factors into the EVM model. We introduced the software rework index (SRI), which is incorporated into the traditional EVM model to enhance its predictability of the software project cost at completion, including the rework cost. We defined the SRI in terms of two factors: product functional complexity and the team competency. Finally, we evaluated the proposed model using a dataset drawn from five actual projects. The results showed a significant enhancement in forecasting accuracy.</span></p>

show abstract

“…The essential part of using artificial intelligence (AI) in software effort estimation is the construction of the Artificial Neural Network (ANN) architecture [11]. ANNs have learning adaptability and are good at modeling complex nonlinear relationships.…”

Section: Introductionmentioning

confidence: 99%

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

Ranković

Ivanović

et al. 2021

Entropy

View full text Add to dashboard Cite

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.

show abstract

Optimizing effort and time parameters of COCOMO II estimation using fuzzy multi-objective PSO

Cited by 19 publications

References 13 publications

Optimized COCOMO parameters using hybrid particle swarm optimization

Optimized COCOMO parameters using hybrid particle swarm optimization

Enhancing software development cost control by forecasting the cost of rework: preliminary study

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

Contact Info

Product

Resources

About