Software defect prediction using Bayesian networks

Okutan, Ahmet; Yıldız, Olcay Taner

doi:10.1007/s10664-012-9218-8

Cited by 260 publications

(160 citation statements)

References 43 publications

(42 reference statements)

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“…Moreover, the studies in [11], [12] discussed various ML techniques and provided the ML capabilities in software defect prediction. The studies assisted the developer to use useful software metrics and suitable data mining technique in order to enhance the software quality.…”

Section: Related Workmentioning

confidence: 99%

Software Bug Prediction using Machine Learning Approach

Hammouri¹,

Hammad²,

Alnabhan³

et al. 2018

ijacsa

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Abstract-Software Bug Prediction (SBP) is an important issue in software development and maintenance processes, which concerns with the overall of software successes. This is because predicting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust bug prediction model is a challenging task and many techniques have been proposed in the literature. This paper presents a software bug prediction model based on machine learning (ML) algorithms. Three supervised ML algorithms have been used to predict future software faults based on historical data. These classifiers are Naïve Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANNs). The evaluation process showed that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance.

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Section: Related Workmentioning

confidence: 99%

Software Bug Prediction using Machine Learning Approach

Hammouri¹,

Hammad²,

Alnabhan³

et al. 2018

ijacsa

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“…Tomcat has been largely used for empirical software engineering studies [6,10,7,8,9,14,18,20]. For example, Okutan et al [11] built a Bayesian network among metrics and defectiveness, to measure which metrics were more important in terms of their effect on defectiveness and to explore the influential relationships among them. The authors used 9 datasets from the teraPromise data repository 9 , considering Tomcat among other systems, and showed that RFC, LOC, and LOCQ are more effective on defect proneness.…”

Section: Related Workmentioning

confidence: 99%

113 times Tomcat: A dataset

Destefanis

Arzoky

Counsell

et al. 2018

Preprint

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Abstract. Measuring software to get information about its properties and quality is one of the main issues in modern software engineering. The aim of this paper is to present a dataset of metrics associated to 113 versions of Tomcat. We describe the dataset along with the adopted criteria and the opportunities of research, providing preliminary results. This dataset can enhance the reliability of empirical studies, enabling their reproducibility, reducing their cost, and it can foster further research on software quality and software metrics.

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“…• Neural Networks In their work, Okutan and Yildiz [5] used a Bayesian networks to determine the set of metrics that are most important and focus on them more to predict defectiveness. They used the Bayesian networks to determine the probabilistic influential relationships among software metrics and defect proneness.…”

Section: Literature Reviewmentioning

confidence: 99%

Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

Mehdi¹

2016

IJCATR

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Defects in modules of software systems is a major problem in software development. There are a variety of data mining techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use a range of software defect prediction models to complement each other rather than relying on a single technique.

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Software defect prediction using Bayesian networks

Cited by 260 publications

References 43 publications

Software Bug Prediction using Machine Learning Approach

Software Bug Prediction using Machine Learning Approach

113 times Tomcat: A dataset

Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

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