Proposing an Enhanced Artificial Neural Network Prediction Model to Improve the Accuracy in Software Effort Estimation

Attarzadeh, Iman; Mehranzadeh, Amin; Barati, Ali

doi:10.1109/cicsyn.2012.39

Cited by 37 publications

(18 citation statements)

References 10 publications

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“…Patil et al [ 19] [20]. The proposed model provides an increase of 8.36% prediction of estimation accuracy compared to COCOMO II.…”

Section: Literature Reviewmentioning

confidence: 86%

Time of Development and Fitness Analysis of Modified COCOMOII Model for Software Projects

Sivakumar¹,

Sureshkumar²

2019

IJRTE

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In the management of software projects, software cost estimate is very important. The absence of precise and premature estimates is a significant source of the inability of many software initiatives. Nevertheless, it remains a challenge for the software industry to correctly estimate the time and the cost of development. It is used to predict how much effort and time the project needs. The precision of the technology proposal usually depends on the estimated time and the strength standard of the template. In this research analysis the time of development and model fitness are analyzed as a major contribution. The optimized coefficient values for A, B, C & D is used to estimate the time of development and model fitness. This experimental analysis proved that the time of development and model fitness analysis shows the great significance in the estimation and their influence in software cost estimation

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“…Patil et al [ 19] [20]. The proposed model provides an increase of 8.36% prediction of estimation accuracy compared to COCOMO II.…”

Section: Literature Reviewmentioning

confidence: 86%

Time of Development and Fitness Analysis of Modified COCOMOII Model for Software Projects

Sivakumar¹,

Sureshkumar²

2019

IJRTE

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“…Attarzadeh et al proposed enhanced ANN model to accommodate COCOMO II post‐architecture model. The difference of their proposed ANN model with the model specified in Wittig and Finnie is that in this study, all EM values have been preprocessed by applying log‐transformation before giving input to ANN to handle vague and broad range of software attributes.…”

Section: Overview Of Selected Studiesmentioning

confidence: 99%

“…Attarzadeh et al 60 SDEE is a complex process, and all software project's features do not have same weightage on development effort of that software. In such situation, ABE does not produce good effort estimation results.…”

Section: Overview Of Selected Studiesmentioning

confidence: 99%

The state‐of‐the‐art in software development effort estimation

Gautam

Singh

2018

J Software Evolu Process

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The software developers and researchers have been facing difficulties regarding software development effort estimation (SDEE) since 1960s. Both overestimation and underestimation are problematic for future software development. The software engineering field is continuously adapting new technologies and development methodologies, so there is always a requirement to have an accurate SDEE method that can cater the needs of continually growing software industry. The major purpose of this state‐of‐the‐art review is to provide an additional insight of existing SDEE studies while considering five points of reference: techniques used to construct models, strengths and weaknesses of different models, availability of benchmark data sets, data set characteristics, generalization ability of models. We have performed a comprehensive review of SDEE studies published in the period 1981‐2016. We have defined a new scheme of categorizing existing SDEE models. We have found that a majority of available data sets do not include complete information of projects, which misleads the direction of research. To compare SDEE models, we recommend to use same data sets while focusing on specific aspects of accuracy as none of SDEE studies has yet been able to compare all the existing models over same data sets while considering same aspects of accuracy.

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“…In [32], author provided a novel artificial neural network (ANN) prediction model which incorporates COCOMO and ANN-COCOMO II, to provide more accurate software estimates at the early phase of software development. ANN was employed to regulate the software features considering historical project data.…”

Section: What Is Ann?mentioning

confidence: 99%

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

Sheta¹,

Kassaymeh²,

Rine³

2014

ijacsa

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Abstract-On time and within budget software project development represents a challenge for software project managers. Software management activities include but are not limited to: estimation of project cost, development of schedules and budgets, meeting user requirements and complying with standards. Recruiting development team members is a sophisticated problem for a software project manager. Since the utmost cost in software development effort is manpower, software project effort and is associated cost estimation models are used in estimating the effort required to complete a project. This effort estimate can then be converted into dollars based on the proper labor rates. An initial development team needs to be selected not only at the beginning of the project but also during the development process. It is important to allocate the necessary team to a project and efficiently distribute their effort during the development life cycle. In this paper, we provide our initial idea of developing a prediction model for defining the estimated required number of test workers of a software project during the software testing process. The developed models utilize the test instance and the number of observed faults as input to the proposed models. Artificial Neural Networks (ANNs) successfully build the dynamic relationships between the inputs and output and produce and accurate predication estimates.

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Proposing an Enhanced Artificial Neural Network Prediction Model to Improve the Accuracy in Software Effort Estimation

Cited by 37 publications

References 10 publications

Time of Development and Fitness Analysis of Modified COCOMOII Model for Software Projects

Time of Development and Fitness Analysis of Modified COCOMOII Model for Software Projects

The state‐of‐the‐art in software development effort estimation

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

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