Classification-tree models of software-quality over multiple releases

Khoshgoftaar, Taghi M.; Allen, Edward B.; Jones, Wendell; Hudepohl, J.P.

doi:10.1109/24.855532

Cited by 69 publications

(25 citation statements)

References 33 publications

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“…This is because, in such a scenario the test data used for evaluating the calibrated quality estimation model, is independent of the training data set (Khoshgoftaar et al, 2000b). Such an approach simulates the application of the model to the currently underdevelopment software release.…”

Section: Scommmentioning

confidence: 99%

Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study

2005

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The primary aim of risk-based software quality classification models is to detect, prior to testing or operations, components that are most-likely to be of high-risk. Their practical usage as quality assurance tools is gauged by the prediction-accuracy and cost-effective aspects of the models. Classifying modules into two risk groups is the more commonly practiced trend. Such models assume that all modules predicted as high-risk will be subjected to quality improvements. Due to the always-limited reliability improvement resources and the variability of the quality risk-factor, a more focused classification model may be desired to achieve costeffective software quality assurance goals. In such cases, calibrating a three-group (high-risk, medium-risk, and low-risk) classification model is more rewarding.We present an innovative method that circumvents the complexities, computational overhead, and difficulties involved in calibrating pure or direct three-group classification models. With the application of the proposed method, practitioners can utilize an existing two-group classification algorithm thrice in order to yield the three risk-based classes. An empirical approach is taken to investigate the effectiveness and validity of the proposed technique. Some commonly used classification techniques are studied to demonstrate the proposed methodology. They include, the C4.5 decision tree algorithm, discriminant analysis, and case-based reasoning. For the first two, we compare the three-group model calibrated using the respective techniques with the one built by applying the proposed method. Any two-group classification technique can be employed by the proposed method, including those that do not provide a direct three-group classification model, e.x., logistic regression and certain binary classification trees, such as CART. Based on a case study of a large-scale industrial software system, it is observed that the proposed method yielded promising results. For a given classification technique, the expected cost of misclassification of the proposed three-group models were significantly better (generally) when compared to the technique's direct three-group model. In addition, the proposed method is also evaluated against an alternate indirect three-group classification method.

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Section: Scommmentioning

confidence: 99%

Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study

2005

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“…Khoshgoftaar et al apply the classification tree models on defects collected from multiple releases of software products to construct a prediction models. The defect models can be applied to locate modules that are likely to cause problems (Khoshgoftaar et al 2000). Although these methods can be used to locate faulty modules, they do not indicate why and how problems occur.…”

Section: Related Workmentioning

confidence: 99%

Improvement of causal analysis using multivariate statistical process control

Chang

Chu

2008

Software Qual J

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Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the outof-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the causal analysis of a software process.

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“…Typical methods include linear or logistic regression [7,15] , classification and regression trees [3,19] , artificial neural networks [1,16] , memorybased methods [20][21] and Bayesian methods [22][23] . In order to further increase the robustness to the outlier in the training data and improve the prediction performance, Guo et al [2] applied ensemble learning to the software defect detection and achieved better performance compared to other commonly-used methods such as logistic regression and decision tree.…”

Section: Software Defect Detectionmentioning

confidence: 99%

“…Machine learning techniques have been successfully applied to building predictive models for software defect detection [1][2][3][4][5][6][7] . The static and dynamic code attributes or software metrics are extracted from each software module to form an example, which is then labeled as "defective" or "defect-free".…”

Section: Introductionmentioning

confidence: 99%

Software Defect Detection with Rocus

Jiang

Zhou

2011

J. Comput. Sci. Technol.

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Software defect detection aims to automatically identify defective software modules for efficient software test in order to improve the quality of a software system. Although many machine learning methods have been successfully applied to the task, most of them fail to consider two practical yet important issues in software defect detection. First, it is rather difficult to collect a large amount of labeled training data for learning a well-performing model; second, in a software system there are usually much fewer defective modules than defect-free modules, so learning would have to be conducted over an imbalanced data set. In this paper, we address these two practical issues simultaneously by proposing a novel semi-supervised learning approach named Rocus. This method exploits the abundant unlabeled examples to improve the detection accuracy, as well as employs under-sampling to tackle the class-imbalance problem in the learning process. Experimental results of real-world software defect detection tasks show that Rocus is effective for software defect detection. Its performance is better than a semi-supervised learning method that ignores the class-imbalance nature of the task and a class-imbalance learning method that does not make effective use of unlabeled data.

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Classification-tree models of software-quality over multiple releases

Cited by 69 publications

References 33 publications

Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study

Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study

Improvement of causal analysis using multivariate statistical process control

Software Defect Detection with Rocus

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