A Novel Study for Summary/attribute Based Bug Tracking Classification Using Latent Semantic Indexing and Svd in Data Mining
نویسندگان
چکیده
This paper presentsa Latent Semantic Indexing (LSI) method for learningBug tracking concepts in document data. Each attribute in a vector provides the mark of participation of the document in data or term in the parallel concept .The objective to describe the concepts summary based, but to be capable to signify the documents and relations in a combined way for showing document-similarity, document-term, and term-term similarities or semantic relationship.Many technique for the implementing our research i.e. NLP, STEMMING, LUD, &SVD to the relevant similarity like bug report bug title, report summary etc., sinceevery bug report, and mined developer’s name who fixed the bug reports from the developers activity data. We processed the mined textual data, and got the term-to-document matrix. Our proposed model different machine learning methods for on the basis of summary and attribute based classification of bug reports which produce the outlier bugs from large amount of data that improved accuracy and efficiency bug classification and relevant base bug indexing which is similar in meaning by using LSI and SVD Technique. Keyword: BTS, LSI, SVD, LUD etc
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