Empirical validation of object-oriented metrics on open source software for fault prediction

Gyimóthy, Tibor; Ferenć, Rudolf; Siket, István

doi:10.1109/tse.2005.112

Cited by 757 publications

(604 citation statements)

References 14 publications

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“…It is used to predict the dependent variable from a set of independent variables to determine the percent of variance in the dependent variable explained by the independent variables [1,7,37]. This technique has been widely applied to the prediction of fault-prone classes [e.g., 11,12,20,26,33,37]. LR is of two types: univariate LR and multivariate LR.…”

Section: Logistic Regression Analysis: Research Methodologymentioning

confidence: 99%

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

Badri

Flageol

2013

Lecture Notes in Computer Science

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Abstract. Software testing plays a crucial role in software quality assurance.Software testing is, however, a time and resource consuming process. It is, therefore, important to estimate as soon as possible the effort required to test software. Unfortunately, little is known about the prediction of the testing effort. The study presented in this paper aims at exploring empirically the prediction of the testing effort from use cases. We address the testing effort from the perspective of test suites size. We used four metrics to characterize the size and complexity of use cases, and three metrics to quantify different perspectives of the size of corresponding test suites. We used the univariate logistic regression analysis to evaluate the individual effect of each use case metric on the size of test suites. The multivariate logistic regression analysis was used to explore the combined effect of the use case metrics. The performance of the prediction models was evaluated using receiver operating characteristic analysis. An experimental study, using data collected from five Java case studies, is reported providing evidence that some of the use case metrics are significant predictors of the size of test suites.

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Section: Logistic Regression Analysis: Research Methodologymentioning

confidence: 99%

Predicting the Size of Test Suites from Use Cases: An Empirical Exploration

Badri

Flageol

2013

Lecture Notes in Computer Science

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“…Furthermore, bug reports with short description and many specific words greatly increase the effectiveness of finding the weak points (the files) of the system. Not only the above presented method can be used to predict the occurrence of a new bug, but a significant change in source code metrics can be also a clue that the relevant source code files contain a potential bug or bugs [9]. Couto et al presented a paper that shows the possible relationship between changed source metrics (used as predictors) and bugs [5].…”

Section: Related Workmentioning

confidence: 99%

“…Calculated product and process metrics can be used for different quality assurance methods as well. One such example can be a development of a quality rating model [1] or another application can be the determination of the correlation between the distribution of the bugs and the calculated metrics [9]. For such purposes a database containing readily extracted software metrics and located bugs provides a great opportunity.…”

Section: Metricsmentioning

confidence: 99%

Characterization of Source Code Defects by Data Mining Conducted on GitHub

Gyimesi

Tóth

et al. 2015

Computational Science and Its Applications -- ICCSA 2015

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Abstract. In software systems the coding errors are unavoidable due to the frequent source changes, the tight deadlines and the inaccurate specifications. Therefore, it is important to have tools that help us in finding these errors. One way of supporting bug prediction is to analyze the characteristics of the previous errors and identify the unknown ones based on these characteristics. This paper aims to characterize the known coding errors. Nowadays, the popularity of the source code hosting services like GitHub are increasing rapidly. They provide a variety of services, among which the most important ones are the version and bug tracking systems. Version control systems store all versions of the source code, and bug tracking systems provide a unified interface for reporting errors. Bug reports can be used to identify the wrong and the previously fixed source code parts, thus the bugs can be characterized by static source code metrics or by other quantitatively measured properties using the gathered data. We chose GitHub for the base of data collection and we selected 13 Java projects for analysis. As a result, a database was constructed, which characterizes the bugs of the examined projects, thus can be used, inter alia, to improve the automatic detection of software defects.

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“…The relationship between the scattering degree and the code fault proneness is not surprising, since the scattering degree causes an increase of the Chidamber and Kemerer Coupling Between Objects (CBO) metric [6], and authors such as Gyimóthy et al [17] and Basili et al [4] found a significant correlation between CBO and fault proneness. Other authors correlated other metrics different from the scattering degree to the source code fault proneness.…”

Section: Crosscutting Concern Fault Pronenessmentioning

confidence: 99%