Enna

Kultur, Yigit; Turhan, Burak; Bener, Ayşe

doi:10.1145/1453101.1453148

Cited by 24 publications

(1 citation statement)

References 17 publications

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“…Song et al [39] provided an ensemble model of five machine learning methods for estimating software development cost. Kultur et al [23] proposed an ensemble model using neural networks. Pahariya et al [29] provided an ensemble model in which the results from computational intelligence techniques are combined by three different methods to calculate the final estimate.…”

Section: Background and Related Workmentioning

confidence: 99%

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Moradbeiky

Bardsiri

Jafari

2020

cai

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Given various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank.

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Section: Background and Related Workmentioning

confidence: 99%

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Moradbeiky

Bardsiri

Jafari

2020

cai

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An extensive evaluation of ensemble techniques for software change prediction

Catolino

Ferrucci

2019

J Software Evolu Process

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Predicting the areas of the source code having a higher likelihood to change in the future represents an important activity to allow developers to plan preventive maintenance operations.For this reason, several change prediction models have been proposed. Moreover, research community demonstrated how different classifiers impact on the performance of devised models as well as classifiers tend to perform similarly even though they are able to correctly predict the change proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we deeper investigated whether the use of ensemble approaches, ie, machine learning techniques able to combine multiple classifiers, can improve the performances of change prediction models. Specifically, we built three change prediction models based on different predictors, ie, product-, process-metrics-, and developer-related factors, comparing the performances of four ensemble techniques (ie, Boosting, Random Forest, Bagging, and Voting) with those of standard machine learning classifiers (ie, Logistic Regression, Naive Bayes, Simple Logistic, and Multilayer Perceptron). The study was conducted on 33releases of 10 open-source systems, and the results showed how ensemble methods and in particular Random Forest provide a significant improvement of more than 10% in terms of F measure. Indeed, the statistical analyses conducted confirm the superiority of this ensemble technique. Moreover, the model built using developer-related factors performed better than the other models that exploit product and process metrics and achieves an overall median of F measure around 77%.

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Applying a general regression neural network for predicting development effort of short-scale programs

López-Martín

2010

Neural Comput & Applic

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Software development effort prediction is considered in several international software processes as the Capability Maturity Model-Integrated (CMMi), by ISO-15504 as well as by ISO/IEC 12207. In this paper, data of two kinds of lines of code gathered from programs developed with practices based on the Personal Software Process (PSP) were used as independent variables in three models for estimating and predicting the development effort. Samples of 163 and 80 programs were used for verifying and validating, respectively, the models. The prediction accuracy comparison among a multiple linear regression, a general regression neural network, and a fuzzy logic model was made using as criteria the magnitude of error relative to the estimate (MER) and mean square error (MSE). Results accepted the following hypothesis: effort prediction accuracy of a general regression neural network is statistically equal than those obtained by a fuzzy logic model as well as by a multiple linear regression, when new and change code and reused code obtained from short-scale programs developed with personal practices are used as independent variables.

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Enna

Cited by 24 publications

References 17 publications

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

An extensive evaluation of ensemble techniques for software change prediction

Applying a general regression neural network for predicting development effort of short-scale programs

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