Neural network based models for software effort estimation: a review

Dave, Vachik S.; Dutta, Kamlesh

doi:10.1007/s10462-012-9339-x

Cited by 120 publications

(45 citation statements)

References 28 publications

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“…Therefore the generated models take advantage of experts knowledge, interoperable, and could be applied to various problems as risk analysis or software quality prediction. ANNs gained noticeable attention by researchers for effort estimation as illustrated by the review in [21], but it is insufficient to generalize the applicability of ANN in effort estimation. The authors stated that it is required to have further thorough investigation.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Software Effort Estimation Models Using Firefly Algorithm

Ghatasheh¹,

Faris²,

Aljarah³

et al. 2015

JSEA

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Software development effort estimation is considered a fundamental task for software development life cycle as well as for managing project cost, time and quality. Therefore, accurate estimation is a substantial factor in projects success and reducing the risks. In recent years, software effort estimation has received a considerable amount of attention from researchers and became a challenge for software industry. In the last two decades, many researchers and practitioners proposed statistical and machine learning-based models for software effort estimation. In this work, Firefly Algorithm is proposed as a metaheuristic optimization method for optimizing the parameters of three COCOMO-based models. These models include the basic COCOMO model and other two models proposed in the literature as extensions of the basic COCOMO model. The developed estimation models are evaluated using different evaluation metrics. Experimental results show high accuracy and significant error minimization of Firefly Algorithm over other metaheuristic optimization algorithms including Genetic Algorithms and Particle Swarm Optimization.

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Section: Related Workmentioning

confidence: 99%

Optimizing Software Effort Estimation Models Using Firefly Algorithm

Ghatasheh¹,

Faris²,

Aljarah³

et al. 2015

JSEA

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“…According to the survey, case-based reasoning (CBR) and artificial neural network (ANN) were the most widely used techniques. In 2014, Dave and Dutta [44] examined existing studies that focus only on neural network.…”

Section: Data Mining In Effort Estimationmentioning

confidence: 99%

Data Mining and Machine Learning for Software Engineering

Kiyak¹

2021

Data Mining - Methods, Applications and Systems

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Software engineering is one of the most utilizable research areas for data mining. Developers have attempted to improve software quality by mining and analyzing software data. In any phase of software development life cycle (SDLC), while huge amount of data is produced, some design, security, or software problems may occur. In the early phases of software development, analyzing software data helps to handle these problems and lead to more accurate and timely delivery of software projects. Various data mining and machine learning studies have been conducted to deal with software engineering tasks such as defect prediction, effort estimation, etc. This study shows the open issues and presents related solutions and recommendations in software engineering, applying data mining and machine learning techniques.

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“…It is also necessary to assess the accuracy of the estimation technique, so that estimation comes as close to the actual cost as possible. This prevents mis-utilization of resources and unnecessary time and money constraints that can affect the resultant quality [106,107]. In this study, various existing techniques for estimation in ASD are assessed, and their accuracy parameters get identified.…”

Section: Ri-1: What Are the Various Estimation Mechanisms Explored Fomentioning

confidence: 99%

A Systematic Review on Software Cost Estimation in Agile Software Development

Saurabh¹,

Sagnika²,

Mishra³

et al. 2017

JESTR

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In the last few years, the size and functionality of software have experienced a massive growth. Along with this, cost estimation plays a major role in the whole cycle of software development, and hence, it is a necessary task that should be done before the development cycle begins and may run throughout the software life cycle. It helps in making accurate estimation for any project so that appropriate charges and delivery date can be obtained. It also helps in identifying the effort required for developing the application, which assures the project acceptance or denial. Since late 90's, Agile Software Development (ASD) methodologies have shown high success rates for projects due to their capability of coping with changing requirements of the customers. Commencing product development using agile methods is a challenging task due to the live and dynamic nature of ASD. So, accurate cost estimation is a must for such development models in order to fine-tune the delivery date and estimation, while keeping the quality of software as the most important priority. This paper presents a systematic survey of cost estimation in ASD, which will be useful for the agile users to understand current trends in cost estimation in ASD.

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Neural network based models for software effort estimation: a review

Cited by 120 publications

References 28 publications

Optimizing Software Effort Estimation Models Using Firefly Algorithm

Optimizing Software Effort Estimation Models Using Firefly Algorithm

Data Mining and Machine Learning for Software Engineering

A Systematic Review on Software Cost Estimation in Agile Software Development

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