Software Engineering is a branch of computers that includes the development of structured softwar... more Software Engineering is a branch of computers that includes the development of structured software applications. Estimation is a significant measure of software engineering projects, and the skill to yield correct effort estimates influences vital economic processes, which include budgeting and bid tenders. But it is challenging to estimate at an initial stage of project development. Numerous conventional and machine learning-based methods are utilized for estimating effort and still, it is a challenge to achieve consistency in precise predictions. In this research exploration, various ANN-based models are compared with conventional algorithmic methods. The study also presents the comparison of results on various datasets from the artificial neural network models, deep learning models, higher-order Neural Network models, leading to the conclusion that hybrid methods yield better results. This paper also includes an analysis of primary data collected from Software Project professiona...
Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a... more Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a process to predict the Effort in terms of cost and time, required to develop a software product. Traditionally, researchers have used the off the shelf empirical models like COCOMO or developed various methods using statistical approaches like regression and analogy based methods but these methods exhibit a number of shortfalls. To predict the effort at early stages is really difficult as very less information is available. To improve the effort estimation accuracy, an alternative is to use machine learning (ML) techniques and many researchers have proposed plethora of such machine learning based models. This paper aims to systematically analyze various machine learning models considering the traits like type of machine learning method used, estimation accuracy gained with that method, dataset used and its comparison with empirical model. Although researchers have started exploring Mach...
Several software effort estimation models have been d veloped over the past few years, but provid... more Several software effort estimation models have been d veloped over the past few years, but providing accurate estimates of the software projects is stil l a challenge. Failures in software are mainly due to the erroneous estimation of effort at early stages. Therefore, ma ny researchers are working on the development of ne w models and the improvement of the existing ones using artificial i ntelligence techniques. Dynamic scenarios of softwa re development technology make effort estimation difficult to pred ict. Ability of ANN (Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost driver s) makes it as a budding tool for estimation. This paper presents a review based on performance analysis of different A NNs and comparing the results of various ANN models in effo rt estimation. Keywords— Effort Estimation, Artificial Neural Network, MMRE, CO C MO, machine learning
Inspite of the several software effort estimation models developed over the last 30 years, provid... more Inspite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Failures in software are mainly due to the erroneous project management practices; one of them is effort estimation. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques. Dynamic scenarios of software development technology make effort estimation more challenging. Ability of ANN (Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a budding tool for estimation. This paper presents a performance analysis of different ANNs and compare the results of various ANN models in effort estimation.
Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a... more Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a process to predict the Effort in terms of cost and time, required to develop a software product. Traditionally, researchers have used the off the shelf empirical models like COCOMO or developed various methods using statistical approaches like regression and analogy based methods but these methods exhibit a number of shortfalls. To predict the effort at early stages is really difficult as very less information is available. To improve the effort estimation accuracy, an alternative is to use machine learning (ML) techniques and many researchers have proposed plethora of such machine learning based models. This paper aims to systematically analyze various machine learning models considering the traits like type of machine learning method used, estimation accuracy gained with that method, dataset used and its comparison with empirical model. Although researchers have started exploring Mach...
For any industry to stay competitive, managing balance between quality and cost of software is im... more For any industry to stay competitive, managing balance between quality and cost of software is important. Estimating software development effort remains a complex problem and one which continues to attract considerable research attention. Number of researchers had made their efforts to produce different modeling techniques in last few decades. This paper is about the comprehensive descriptive exploration of the techniques that were presented in software effort estimation field. In this paper we present the main findings of few research papers that have utilized a various parametric and non parametric techniques amalgamated with computational intelligence technique, in software effort estimation. All widespread models discussed at one place will give researchers a prospect to comprehend the pros and cons, similarities and the differences among various models.
Software Project effort estimation provides the estimation of various resources, effort and time ... more Software Project effort estimation provides the estimation of various resources, effort and time vitally required to complete a project. Accurateness in effort estimation is significant for developers and clients.The present study proposes to develop a machine learning based model to predict Software Project effort estimation.Paper attempts to evaluate the training of Artificial Neural Network using population based Genetic Algorithm. The code is generated in MATLAB for training artificial neural network of 22 input features of COCOMO dataset and single target feature. The COCOMO I, NASA98 data sets; and four project data from a software company were used in the evaluation of the proposed neuro Genetic COCOMO II (GANNCOCOMO II). The assessment of the attained results, using Mean of Magnitude of Relative Error (MMRE) and PRED(25%) evaluation methods, indicate that the GANN-COCOMO II produced the MMRE less than the original COCOMO and the value of PRED(25%) in the GANN-COCOMO II is hi...
International Journal of Advanced Computer Science and Applications
Organizations are struggling to deliver the expected software functionality and quality in schedu... more Organizations are struggling to deliver the expected software functionality and quality in scheduled time and prescribed budget. Despite availability of numerous advanced effort estimation techniques overestimation and underestimation occur on a vast scale and results in project failures and significant loss to the organization. The paper proposes machine learning based approach to calculate the optimized effort and level of confidence. Genetically trained neural network evaluates the optimum effort for given COCOMO II variables. The level of confidence is evaluated by fuzzy logic and indicates the percentage that the predicted effort will not exceed the limits.
Keywords:-software effort estimation, algorithmic models, software Metrics; line of code, computa... more Keywords:-software effort estimation, algorithmic models, software Metrics; line of code, computational intelligence, neural networks, back propagation, mean magnitude of relative error.
Software Engineering is a branch of computers that includes the development of structured softwar... more Software Engineering is a branch of computers that includes the development of structured software applications. Estimation is a significant measure of software engineering projects, and the skill to yield correct effort estimates influences vital economic processes, which include budgeting and bid tenders. But it is challenging to estimate at an initial stage of project development. Numerous conventional and machine learning-based methods are utilized for estimating effort and still, it is a challenge to achieve consistency in precise predictions. In this research exploration, various ANN-based models are compared with conventional algorithmic methods. The study also presents the comparison of results on various datasets from the artificial neural network models, deep learning models, higher-order Neural Network models, leading to the conclusion that hybrid methods yield better results. This paper also includes an analysis of primary data collected from Software Project professiona...
Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a... more Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a process to predict the Effort in terms of cost and time, required to develop a software product. Traditionally, researchers have used the off the shelf empirical models like COCOMO or developed various methods using statistical approaches like regression and analogy based methods but these methods exhibit a number of shortfalls. To predict the effort at early stages is really difficult as very less information is available. To improve the effort estimation accuracy, an alternative is to use machine learning (ML) techniques and many researchers have proposed plethora of such machine learning based models. This paper aims to systematically analyze various machine learning models considering the traits like type of machine learning method used, estimation accuracy gained with that method, dataset used and its comparison with empirical model. Although researchers have started exploring Mach...
Several software effort estimation models have been d veloped over the past few years, but provid... more Several software effort estimation models have been d veloped over the past few years, but providing accurate estimates of the software projects is stil l a challenge. Failures in software are mainly due to the erroneous estimation of effort at early stages. Therefore, ma ny researchers are working on the development of ne w models and the improvement of the existing ones using artificial i ntelligence techniques. Dynamic scenarios of softwa re development technology make effort estimation difficult to pred ict. Ability of ANN (Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost driver s) makes it as a budding tool for estimation. This paper presents a review based on performance analysis of different A NNs and comparing the results of various ANN models in effo rt estimation. Keywords— Effort Estimation, Artificial Neural Network, MMRE, CO C MO, machine learning
Inspite of the several software effort estimation models developed over the last 30 years, provid... more Inspite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Failures in software are mainly due to the erroneous project management practices; one of them is effort estimation. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques. Dynamic scenarios of software development technology make effort estimation more challenging. Ability of ANN (Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a budding tool for estimation. This paper presents a performance analysis of different ANNs and compare the results of various ANN models in effort estimation.
Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a... more Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a process to predict the Effort in terms of cost and time, required to develop a software product. Traditionally, researchers have used the off the shelf empirical models like COCOMO or developed various methods using statistical approaches like regression and analogy based methods but these methods exhibit a number of shortfalls. To predict the effort at early stages is really difficult as very less information is available. To improve the effort estimation accuracy, an alternative is to use machine learning (ML) techniques and many researchers have proposed plethora of such machine learning based models. This paper aims to systematically analyze various machine learning models considering the traits like type of machine learning method used, estimation accuracy gained with that method, dataset used and its comparison with empirical model. Although researchers have started exploring Mach...
For any industry to stay competitive, managing balance between quality and cost of software is im... more For any industry to stay competitive, managing balance between quality and cost of software is important. Estimating software development effort remains a complex problem and one which continues to attract considerable research attention. Number of researchers had made their efforts to produce different modeling techniques in last few decades. This paper is about the comprehensive descriptive exploration of the techniques that were presented in software effort estimation field. In this paper we present the main findings of few research papers that have utilized a various parametric and non parametric techniques amalgamated with computational intelligence technique, in software effort estimation. All widespread models discussed at one place will give researchers a prospect to comprehend the pros and cons, similarities and the differences among various models.
Software Project effort estimation provides the estimation of various resources, effort and time ... more Software Project effort estimation provides the estimation of various resources, effort and time vitally required to complete a project. Accurateness in effort estimation is significant for developers and clients.The present study proposes to develop a machine learning based model to predict Software Project effort estimation.Paper attempts to evaluate the training of Artificial Neural Network using population based Genetic Algorithm. The code is generated in MATLAB for training artificial neural network of 22 input features of COCOMO dataset and single target feature. The COCOMO I, NASA98 data sets; and four project data from a software company were used in the evaluation of the proposed neuro Genetic COCOMO II (GANNCOCOMO II). The assessment of the attained results, using Mean of Magnitude of Relative Error (MMRE) and PRED(25%) evaluation methods, indicate that the GANN-COCOMO II produced the MMRE less than the original COCOMO and the value of PRED(25%) in the GANN-COCOMO II is hi...
International Journal of Advanced Computer Science and Applications
Organizations are struggling to deliver the expected software functionality and quality in schedu... more Organizations are struggling to deliver the expected software functionality and quality in scheduled time and prescribed budget. Despite availability of numerous advanced effort estimation techniques overestimation and underestimation occur on a vast scale and results in project failures and significant loss to the organization. The paper proposes machine learning based approach to calculate the optimized effort and level of confidence. Genetically trained neural network evaluates the optimum effort for given COCOMO II variables. The level of confidence is evaluated by fuzzy logic and indicates the percentage that the predicted effort will not exceed the limits.
Keywords:-software effort estimation, algorithmic models, software Metrics; line of code, computa... more Keywords:-software effort estimation, algorithmic models, software Metrics; line of code, computational intelligence, neural networks, back propagation, mean magnitude of relative error.
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