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- W2364300190 abstract "Automatic classification of software bug reports save a large number of time and human resources. However,the bug reports submitted by users have a strong subjectivity,with casual text descriptions. This results in ineffective classification. Two improved algorithms are proposed to reduce feature dimensions in classifying bug reports from their text descriptions. These two algorithms are based on the traditional Term Frequency-Inverse Document Frequency(TFIDF)algorithm,combined with the term frequency in documentations and the distribution of the term in the same category and different types of categories. One weight processing is used after feature dimension reduction in order to get a better result. Experimental results indicate that the proposed algorithm has better performance in term of precision,recall,F1 score,and accuracy than the current algorithms." @default.
- W2364300190 created "2016-06-24" @default.
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- W2364300190 date "2015-01-01" @default.
- W2364300190 modified "2023-09-23" @default.
- W2364300190 title "Improved Automatic Classification Algorithm of Software Bug Report" @default.
- W2364300190 hasPublicationYear "2015" @default.
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