Christofer Silva - Academia.edu (original) (raw)
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Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS)
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Papers by Christofer Silva
Software Quality Journal, 2013
Despite the interest and the increasing number of static analysis tools for detecting defects in ... more Despite the interest and the increasing number of static analysis tools for detecting defects in software systems, there is still no consensus on the actual gains that such tools introduce in software development projects. Therefore, this article reports a study carried out to evaluate the degree of correspondence and correlation between postrelease defects (i.e., field defects) and warnings issued by FindBugs, a bug finding tool widely used in Java systems. The study aimed to evaluate two types of relations: static correspondence (when warnings contribute to find the static program locations changed to remove field defects) and statistical correlation (when warnings serve as early indicators for future field defects). As a result, we have concluded that there is no static correspondence between field defects and warnings. However, statistical tests showed that there is a moderate level of correlation between warnings and such kinds of software defects.
2012 16th European Conference on Software Maintenance and Reengineering, 2012
Bug prediction is an important challenge for software engineering research. It consist in looking... more Bug prediction is an important challenge for software engineering research. It consist in looking for possible early indicators of the presence of bugs in a software. However, despite the relevance of the issue, most experiments designed to evaluate bug prediction only investigate whether there is a linear relation between the predictor and the presence of bugs. However, it is well known that standard regression models cannot filter out spurious relations. Therefore, in this paper we describe an experiment to discover more robust evidences towards causality between software metrics (as predictors) and the occurrence of bugs. For this purpose, we have relied on Granger Causality Test to evaluate whether past changes in a given time series are useful to forecast changes in another series. As its name suggests, Granger Test is a better indication of causality between two variables. We present and discuss the results of experiments on four real world systems evaluated over a time frame of almost four years. Particularly, we have been able to discover in the history of metrics the causes -in the terms of the Granger Test -for 64% to 93% of the defects reported for the systems considered in our experiment.
Software Quality Journal, 2013
Despite the interest and the increasing number of static analysis tools for detecting defects in ... more Despite the interest and the increasing number of static analysis tools for detecting defects in software systems, there is still no consensus on the actual gains that such tools introduce in software development projects. Therefore, this article reports a study carried out to evaluate the degree of correspondence and correlation between postrelease defects (i.e., field defects) and warnings issued by FindBugs, a bug finding tool widely used in Java systems. The study aimed to evaluate two types of relations: static correspondence (when warnings contribute to find the static program locations changed to remove field defects) and statistical correlation (when warnings serve as early indicators for future field defects). As a result, we have concluded that there is no static correspondence between field defects and warnings. However, statistical tests showed that there is a moderate level of correlation between warnings and such kinds of software defects.
2012 16th European Conference on Software Maintenance and Reengineering, 2012
Bug prediction is an important challenge for software engineering research. It consist in looking... more Bug prediction is an important challenge for software engineering research. It consist in looking for possible early indicators of the presence of bugs in a software. However, despite the relevance of the issue, most experiments designed to evaluate bug prediction only investigate whether there is a linear relation between the predictor and the presence of bugs. However, it is well known that standard regression models cannot filter out spurious relations. Therefore, in this paper we describe an experiment to discover more robust evidences towards causality between software metrics (as predictors) and the occurrence of bugs. For this purpose, we have relied on Granger Causality Test to evaluate whether past changes in a given time series are useful to forecast changes in another series. As its name suggests, Granger Test is a better indication of causality between two variables. We present and discuss the results of experiments on four real world systems evaluated over a time frame of almost four years. Particularly, we have been able to discover in the history of metrics the causes -in the terms of the Granger Test -for 64% to 93% of the defects reported for the systems considered in our experiment.