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• W2974766070 abstract "While most of the existing class stability assessors just rely on structural information retrieved from a desired source code snapshot. However, class stability is intrinsically characterized by the evolution of a number of dependencies and change propagation factors which aid to promote the ripple effect. Identification of classes prone to ripple effect (instable classes) through mining the version history of change propagation factors can aid developers to reduce the efforts needed to maintain and evolve the system. We propose Historical Information for Class Stability Prediction (HICSP), an approach to exploit change history information to predict the instable classes based on its correlation with change propagation factors. Subsequently, we performed two empirical studies. In the first study, we evaluate the HICSP on the version history of 10 open source projects. Subsequently, in the second replicated study, we evaluate the effectiveness of HICSP by tuning the parameters of its stability assessors. We observed the 4 to 16 percent improvement in term of F-measure value to predict the instable classes through HICSP as compared to existing class stability assessors. The promising results indicate that HICSP is able to identify instable classes and can aid developers in their decision making." @default.
• W2974766070 created "2019-09-26" @default.
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• W2974766070 date "2019-09-16" @default.
• W2974766070 modified "2023-09-26" @default.
• W2974766070 title "Mining version history to predict the class instability" @default.
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• W2974766070 doi "https://doi.org/10.1371/journal.pone.0221780" @default.
• W2974766070 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6746542" @default.
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