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• W2135641621 abstract "Long-running software systems tend to show degraded performance and an increased failure occurrence rate. This problem, known as Software Aging, which is typically related to the runtime accumulation of error conditions, is caused by the activation of the so-called Aging-Related Bugs (ARBs). This paper aims to predict the location of Aging-Related Bugs in complex software systems, so as to aid their identification during testing. First, we carried out a bug data analysis on three large software projects in order to collect data about ARBs. Then, a set of software complexity metrics were selected and extracted from the three projects. Finally, by using such metrics as predictor variables and machine learning algorithms, we built fault prediction models that can be used to predict which source code files are more prone to Aging-Related Bugs." @default.
• W2135641621 created "2016-06-24" @default.
• W2135641621 creator A5034602812 @default.
• W2135641621 creator A5052961846 @default.
• W2135641621 creator A5072680843 @default.
• W2135641621 date "2013-03-01" @default.
• W2135641621 modified "2023-09-30" @default.
• W2135641621 title "Predicting aging-related bugs using software complexity metrics" @default.
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• W2135641621 doi "https://doi.org/10.1016/j.peva.2012.09.004" @default.
• W2135641621 hasPublicationYear "2013" @default.
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