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• W2783803218 abstract "Large software applications encompass many components with complex interdependencies. When a failure occurs, developers usually have limited information and time in their disposal for localizing the root cause of the observed failure. The most common information developers have readily access to includes failure reports, stack traces, and event logs. In this context, a major challenge is to devise techniques that assist developers utilize this information in order to zero-in their focus on specific methods that have a high probability of containing the root cause of the observed failure. Once such an initial set of methods has been identified, other more elaborate, complex, and computationally expensive data flow analyses could be applied. In this paper, we present a technique which aims to identify such an initial set of suspicious methods by first, retrieving information from failure reports obtained from Bugzilla repositories, second by combining this information with graph models that denote actual dependencies obtained from the subject system's source code in order to create an hypothesis space and third, by applying a ranking score to identify methods that have high likelihood of containing the root cause. The technique is shown to be tractable when applied to systems with several thousands of source code methods and the results exhibit high accuracy." @default.
• W2783803218 created "2018-01-26" @default.
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• W2783803218 date "2017-11-06" @default.
• W2783803218 modified "2023-09-24" @default.
• W2783803218 title "Assisting developers towards fault localization by analyzing failure reports" @default.
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• W2783803218 hasPublicationYear "2017" @default.
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