Software effort estimation based on the optimal Bayesian belief network

Zare, Fatemeh; Zare, Hakimeh; Nezhad, Mohammad Saber Fallah

doi:10.1016/j.asoc.2016.08.004

Cited by 45 publications

(24 citation statements)

References 14 publications

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“…Metode-metode machine learning yang telah digunakan untuk estimasi usaha pengembangan perangkat lunak diantaranya k-Nearnest Neighbor (k-NN) [11], [12], [13], [14]. Artificial Neural Networks (ANN) atau Neural Network (NN) [11], [15], [16], Support Vector Machines [11], [17], Naive Bayes (NB) [18], Bayesian Networks (BN) [19], Decision Trees (DT) [12], Linear Regression (LR) [20].…”

Section: Redaksi 1 Pendahuluan 11 Latar Belakangmentioning

confidence: 99%

Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

Kurniawan¹,

Abror²

2019

Explore. jurnal. sistem. inf. dan. telematika

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The methods for Software Effort Estimation are divided into two, these methods are grouped into Non Machine Learning (non-ML) and Machine Learning (ML) methods [1]. The k-NN method has the disadvantage of being unable to tolerate irrelevant features and greatly affect the accuracy of k-NN. The k-NN method is also difficult to deal with missing data problems and feature categorization problems such as features that are not relevant, weight features that are not optimal, and the same features [2]. Whereas the dataset of Software Effort Estimation still has some serious challenges such as the characteristics of the data set, which are irrelevant features and the level of influence of each feature in the estimated data of the software effort [3]. This study compared the k-NN individual method with the combination of feature selection method with k-NN to find out which method was the best. The results showed that the Forward Selection (FS) method and Median Weighted Information Gain with k-Nearest Neighbor can overcome the problem of irrelevant features so as to increase accuracy in the RMSE Software Effort Estimation dataset, which is smaller in the Albrecht dataset of 5,953 using the Median method-WIG k-NN, the Miyazaki dataset is 55,421 and Kemerer is 123,081 using the FS k-NN method. The combination of kNN with Feature Selection is proven to be able to improve the estimation results better than kNN individuals. With the FS k-NN method being the best by winning in 2 datasets Miyazaki and Kemerer.

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Section: Redaksi 1 Pendahuluan 11 Latar Belakangmentioning

confidence: 99%

Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

Kurniawan¹,

Abror²

2019

Explore. jurnal. sistem. inf. dan. telematika

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“…Software cost estimation incorporates the process of reaching a conclusion regarding the amount of effort required for the development of a software system [2]. The most important demanding requirement of the software development cost estimation is the accuracy of the estimation [3]. That is due to the fact that the overestimation of the software development cost might cause losing a project in a tender and, on the other hand, the underestimation might also cause the software company to be incurred by losses and/or it might cause the allocation of lower resources, as a result of which the project quality cannot be guaranteed [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…There are different methods for the estimation of software development effort, the most important of which can be categorised into two sets of model-based and expert-based [3]. The model-based group, as well, is classified into two sets of statistical and mathematical methods, such as regression, and smart methods, e.g., machine learning [17].…”

Section: Introductionmentioning

confidence: 99%

A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques

Khazaiepoor

Bardsiri

Keynia

2019

Applied Computer Systems

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During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the preliminary development stages. These studies have resulted in the offering of a great many of the models. Despite the large deal of efforts, the substantial problems of the offered methods are their dependency on the used data collection and, sometimes, their lack of appropriate efficiency. The current article attempts to present a model for software development effort estimation through making use of evolutionary algorithms and neural networks. The distinctive characteristic of this model is its lack of dependency on the collection of data used as well as its high efficiency. To evaluate the proposed model, six different data collections have been used in the area of software effort estimation. The reason for the application of several data collections is related to the investigation of the model performance independence of the data collection used. The evaluation scales have been MMRE, MdMRE and PRED (0.25). The results have indicated that the proposed model, besides delivering high efficiency in contrast to its counterparts, produces the best responses for all of the used data collections.

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“…The probabilistic safety assessment of digital reactor protection systems (RPSs) has been an interest of many researches due to the ambiguity over the mechanism of the software failure and quantification of the failure probability [1,9]. There are many approaches and methods developed for qualitative and quantitative software reliability assessment.…”

Section: Introduction mentioning

confidence: 99%

“…To address these challenges, Bayesian networks (BN) had been proposed in some studies as an alternative to traditional reliability estimation approaches [4,9,15]. The BN method evaluates the software development life cycle activities for estimating the potential number of remaining faults in the software [9,15]. The BN has been increasingly recognized as a potentially powerful solution to complex risk assessment problems.…”

Section: Introduction mentioning

confidence: 99%

A Bayesian Network Approach to Estimating Software Reliability of RSG-GAS Reactor Protection System

2019

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Reliability represents one of the most important attributes of software quality. Assessing the reliability of software embedded in the safety of highly critical systems is essential. Unfortunately, there are many factors influencing software reliability that cannot be measured directly. Furthermore, the existing models and approaches for assessing software reliability have assumptions and limitations which are not directly acceptable for all systems, such as reactor protection systems. This paper presents the result of a study which aims to conduct quantitative assessment of the software reliability at the reactor protection system (RPS) of RSG-GAS based on software development life cycle. A Bayesian network (BN) is applied in this research and used to predict the software defect in the operation which represents the software reliability. The availability of operation failure data, characteristics of the RPS components and their operation features, prior knowledge on the software development and system reliability, as well as relevant finding from references were considered in the assessment and the construction of nodes on causal network model. The structure of causal model consists of eight nodes including design quality, problem complexity, and defect inserted in the software. The calculation result using Agenarisk software revealed that software defect in the operation of RPS follows binomial statistic distribution with the mean of 1.393. This number indicated the high software maturity level and high capability of the organization. The improvement of software defect concentration range on the posterior distribution compared with the prior's is also identified. The result achieved is valuable for further reliability estimation by introducing new evidence and experience data, and by setting up an appropriate plan in order to enhance software reliability in the RPS.

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Software effort estimation based on the optimal Bayesian belief network

Cited by 45 publications

References 14 publications

Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

Komparasi Metode Kombinasi Seleksi Fitur Dan Machine Learning K-Nearest Neighbor Pada Dataset Label Hours Software Effort Estimation

A Dataset-Independent Model for Estimating Software Development Effort Using Soft Computing Techniques

A Bayesian Network Approach to Estimating Software Reliability of RSG-GAS Reactor Protection System

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