Detection of fault-prone program modules in a very large telecommunications system

Khoshgoftaar, Taghi M.; Allen, Edward B.; Kalaichelvan, Kalai; Goel, Nishith; Hudepohl, J.P.; Mayrand, Jean

doi:10.1109/issre.1995.497640

Cited by 23 publications

(10 citation statements)

References 17 publications

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“…The analysis showed a less good result than in the 1992 study, with a Type II misclassification rate of 19% for one model and 15% for another model [16].…”

Section: Results From Similar Studiescontrasting

confidence: 58%

“…Type II misclassification means that a faulty module was classified as a zero-fault module, thus causing a misdirection of efforts in testing, and possibly causes a fault-prone class to slip through testing [16]. In the same investigation they also investigated a 327 module system with predictive measurements like Halstead's unique operators and operands, McCabe's cyclomatic complexity, source lines of code and commented lines of code.…”

Section: Results From Similar Studiesmentioning

confidence: 99%

“…The main statistical technique for data reduction in fault prediction of software is factor analysis using principal component analysis [15,16,17]. Principal component analysis works as an identification of underlying explanatory variables for the basic variables based on their ability to explain the maximum variance of all basic variables, resulting in a group of unnamed orthogonal variables built from the basic variables [18].…”

Section: Models For Identification Of Fault-prone Modulesmentioning

confidence: 99%

“…Regardless of method, once clustering is done, it is possible to manually seek the upper and lower boundary values for all participating variables in each group defined. Discriminant analysis is a way to establish which boundaries separate different groups, and aims at reducing the risk of misclassification, so in cases where software engineering researchers have tried to separate fault-prone from non-fault-prone modules, this has become the classification method of choice [15,16,17].…”

Section: Models For Identification Of Fault-prone Modulesmentioning

confidence: 99%

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Generalizing fault contents from a few classes

Dieste

Padua

2007

First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)

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The challenges in fault prediction today are to get a prediction as early as possible, at as low a cost as possible, needing as little data as possible and preferably in such a language that your average developer can understand where it came from. This paper presents a fault sampling method where a summary of a few, easily available metrics is used together with the results of a few sampled classes to generalize the fault content to an entire system. The method is tested on a large software system written in Java, that currently consists of around 2 000 classes and 300 000 lines of code. The evaluation shows that the fault generalization method is good at predicting fault-prone clusters and that it is possible to generalize the values of a few representative classes.

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“…The analysis showed a less good result than in the 1992 study, with a Type II misclassification rate of 19% for one model and 15% for another model [16].…”

Section: Results From Similar Studiescontrasting

confidence: 58%

Section: Results From Similar Studiesmentioning

confidence: 99%

Section: Models For Identification Of Fault-prone Modulesmentioning

confidence: 99%

Section: Models For Identification Of Fault-prone Modulesmentioning

confidence: 99%

See 2 more Smart Citations

Generalizing fault contents from a few classes

Dieste

Padua

2007

First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)

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“…Discriminant analysis, a statistical technique used to categorize programs into groups based on the metric values, has been used as a tool for the detection of fault-prone programs [14,17,21]. Munson et al.…”

Section: Related Workmentioning

confidence: 99%

Static analysis tools as early indicators of pre-release defect density

Nagappan¹,

Ball²

Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005.

106

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During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring further testing. We present an empirical approach for the early prediction of pre-release defect density based on the defects found using static analysis tools. The defects identified by two different static analysis tools are used to fit and predict the actual pre-release defect density for Windows Server 2003. We show that there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance. Discriminant analysis shows that the results of static analysis tools can be used to separate high and low quality components with an overall classification rate of 82.91%.

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