文章基本信息

标题：Bug Detection through Text Data Mining
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作者：V. Neelima ; Annapurna. N ; V. Alekhya 等
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：5
出版社：S.S. Mishra
摘要：Text Mining is an interdisciplinary field that draws on information retrieval, Data Mining, machine learning, statistics, and computational linguistics. As most information is currently stored as text, Text Mining is believed to have a high commercial potential value. Text mining, sometimes alternately referred to as text data mining, roughly equivalent to text analysis, refers to the process of deriving high-quality information from text. A typical application is to scan a set of documents written in a natural language and either model the document set for predictive classification purposes or populate a database or search index with the information extracted. One such application of Text Data Mining is bug detection. Bugs are hard to find and usually winds up programmer. In order to alleviate the overhead in debugging, we present an approach to detect bugs in C programs via matching and mining techniques. The input for the system is a text file containing errors, principally syntax errors from a C program which is matched for similar but slightly different code fragments in the database that acts a repository of errors aptly classify them and generate an analysis report predicting solutions for the same.
关键词：Text Data Mining; Knutt-Morris-Pratt algorithm; Syntax Error Correction; Classification; Analysis ;Report