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Fault localization is a challenging task in domain specific data mining. Most existing works focus on call graph that can find bugs which are associated with control flow. However, there are a lot of bugs related to data flow. In this paper, we presented a data dependency graph in fault localization. The approach at first analyzes the execution of the test suites dynamically, then derives the data...
In the current demonstration we present a new instrument that provides for each existing class in an analyzed system information related to the problems the class reveals. We associate different types of problems to a class: design flaws, vulnerabilities and defects. In order to validate its usefulness, we perform some experiments on a suite of object-oriented systems and some results are briefly...
We mined the issue database of GNOME to assess how issues are handled. How many issues are submitted and resolved? Does the backlog grow or decrease? How fast are issues resolved? Does issue resolution speed increase or decrease over time? In which subproject are issues handled most efficiently? To answer such questions, we apply several visualization and quantification instruments to the raw issue...
Detecting latent errors is a key challenging issue in the software testing process. Latent errors could be best detected by bug predictors. A bug predictor manifests the effect of a bug on the program execution state. The aim has been to find the smallest reasonable subset of the bug predictors, manifesting all possible bugs within a program. In this paper, a new algorithm for finding the smallest...
Statistical debugging aims to automate the process of isolating bugs by profiling several runs of the program and using statistical analysis to pinpoint the likely causes of failure. In this paper, we investigate the impact of using richer program profiles such as path profiles on the effectiveness of bug isolation. We describe a statistical debugging tool called HOLMES that isolates bugs by finding...
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