Defect Prediction on Unlabeled Datasets by Using Unsupervised Clustering

Yang, Jingwen; Qian, Hongbing

doi:10.1109/hpcc-smartcity-dss.2016.0073

Cited by 16 publications

(14 citation statements)

References 32 publications

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“…The work of graphics engines is not the primary concern for VR application developers. Defining scene graphs for organizing 3D objects in a VR world, managing virtual users, controlling sensors for detecting events such as object collision and processing events for reacting to user inputs are some of the typical elements of VR systems that the developers should be concerned about [Zhao, 2009].…”

Section: What Should Be Tested?mentioning

confidence: 99%

Towards the Systematic Testing of Virtual Reality Programs

Andrade

Nunes

Delamaro

2019

2019 21st Symposium on Virtual and Augmented Reality (SVR)

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Software testing is a critical activity to ensure that software complies with its specification. However, current software testing activities tend not to be completely effective when applied in specific software domains in Virtual Reality (VR) that has several new types of features such as images, sounds, videos, and differentiated interaction, which can become sources of new kinds of faults. This paper presents an overview of the main VR characteristics that can have an impact on verification, validation, and testing (VV&T). Furthermore, it analyzes some of the most successful VR open-source projects to draw a picture concerning the danger of the lack of software testing activities. We compared the current state of software testing practice in open-source VR projects and evaluate how the lack of testing can be damaging to the development of a product. We assessed the incidence of code smells and verified how such projects behave concerning the tendency to present faults. We also perform the same analyses on projects that are not VR related to have a better understanding of these results. The results showed that the practice of software testing is not yet widespread in the development of VR applications. It was also found that there is a high incidence of code smells in VR projects. Analyzing Non-VR projects we noticed that classes that have test cases tend to produce fewer smells compared to classes that were not tested. Regarding fault-proneness analysis, we used an unsupervised approach to VR and Non-VR projects. Results showed that about 12.2% of the classes analyzed in VR projects are fault-prone, while Non-VR projects presented a lower fault-proneness rate (8.9%). Regarding the application of software testing techniques on VR projects, it was observed that only a small number of projects are concerned about developing test cases for VR projects, perhaps because we still do not have the necessary tools to help in this direction. Concerning smells, we concluded that there is a high incidence in VR projects, especially regarding implementing smells and this high incidence can have a significant influence on faults. Finally, the study related to fault proneness pointed out that the lack of software testing activity is a significant risk to the success of the projects.

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Section: What Should Be Tested?mentioning

confidence: 99%

Towards the Systematic Testing of Virtual Reality Programs

Andrade

Nunes

Delamaro

2019

2019 21st Symposium on Virtual and Augmented Reality (SVR)

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“…A classifier based on a "clustering algorithm" and a "decision tree" or "neural network "are being utilized to recognize anomalous events of detected common incidents for the prediction [11], [12]. If a defect is found, the classifier labels the defect path to systematize the classifier.…”

Section: Defect Prediction Using Classifiersmentioning

confidence: 99%

“…Some classification criteria generally use "NaiveBayes" and "Bagging". The Bayesian classification is a "supervised learning method" and is a "statistical method" for classification [12]. It represents a basic probability model that can capture uncertainty in a model of reason that determines the probability of a result.…”

Section: Defect Prediction Using Classifiersmentioning

confidence: 99%

“…It represents a basic probability model that can capture uncertainty in a model of reason that determines the probability of a result. A recent study [7], [8], [10], [12] in this province is proposed without a secondary supervisory model to capture the regular code of behavioral probability distributions in each region to recognize incidents when they behave abnormally. This information is utilized to filter more than the labeling gives to the positioning algorithm to focus on abnormal observations.…”

Section: Defect Prediction Using Classifiersmentioning

confidence: 99%

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Defects Prediction and Prevention Approaches for Quality Software Development

Memon¹,

Baloch²,

Memon³

et al. 2018

ijacsa

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Abstract-The demand for distributed and complex business applications in the enterprise requires error-free and highquality application systems. Unfortunately, most of the developed software contains certain defects which cause failure of a system. Such failures are unacceptable for the development in the critical or sensitive applications. This makes the development of high quality and defect free software extremely important in software development. It is important to better understand and compute the association among software defects and its failures for the effective prediction and elimination of these defects to decline the failure and improve software quality. This paper presents a review of software defects prediction and its prevention approaches for the quality software development. It also focuses a review on the potential and constraints of those mechanisms in quality product development and maintenance.

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“…If there are no lacks in the training dataset availability, the supervised approach is the main alternative in the software prediction model development. Otherwise, the unsupervised approach offers an alternative solution to address the training dataset availability issue [9,10].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised software defect prediction using median absolute deviation threshold based spectral classifier on signed Laplacian matrix

2019

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Area of interest: The trend of current software inevitably leads to the big data era. There are much of large software developed from hundreds to thousands of modules. In software development projects, finding the defect proneness manually on each module in large software dataset is probably inefficient in resources. In this task, the use of a software defect prediction model becomes a popular solution with much more cost-effective rather than manual reviews. This study presents a specific machine learning algorithm, which is the spectral classifier, to develop a software defect prediction model using unsupervised learning approach. Background and objective: The spectral classifier has been successfully used in software defect prediction because of its reliability to consider the similarities between software entities. However, there are conditional issues when it uses the zero value as partitioning threshold. The classifier will produce the predominantly cluster when the eigenvector values are mostly positives. Besides, it will also generate low clusters compactness when the eigenvector contains outliers. The objective of this study is mainly to propose an alternative partitioning threshold in dealing with the zero threshold issues. Generally, the proposed method is expected to improve the spectral classifier based software defect prediction performances. Methods: This study proposes the median absolute deviation threshold based spectral classifier to carry out the zero value threshold issues. The proposed method considers the eigenvector values dispersion measure as the new partitioning threshold, rather than using a central tendency measure (e.g., zero, mean, median). The baseline method of this study is the zero value threshold based spectral classifier. Both methods are performed on the signed Laplacian matrix to meet the non-negative Laplacian graph assumption. For classification, the heuristic row sum method is used to assign the entity class as the prediction label. Results and conclusion: In terms of clustering, the proposed method can produce better cluster memberships that affect the cluster compactness and the classifier performances improvement. The cluster compactness average of both the proposed and baseline methods are 1.4 DBI and 1.8 DBI, respectively. In classification performance, the proposed method performs better accuracy with lower error rates than

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Defect Prediction on Unlabeled Datasets by Using Unsupervised Clustering

Cited by 16 publications

References 32 publications

Towards the Systematic Testing of Virtual Reality Programs

Towards the Systematic Testing of Virtual Reality Programs

Defects Prediction and Prevention Approaches for Quality Software Development

Unsupervised software defect prediction using median absolute deviation threshold based spectral classifier on signed Laplacian matrix

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