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ABSTRACT
Natural language processing (NLP), as a theory motivated computational technique, has extensive applications. Automated test case generation based on path coverage, which is a popular structural testing activity, can automatically reveal logic defects that exist in NLP programs and can save testing consumption. The high complexity behind SQL language and database schemas has made database querying a challenging task to human programmers. In this paper, we present our new natural language based database querying system as
an alternative solution, by designing new translation models smoothly fusing deep learning and traditional database parsing techniques. We develop new techniques to enable the augmented neural network to reject queries irrelevant to the contents of the target database and recommend candidate queries reversely transformed into natural language.
