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Code reviews are one of the effective methods to estimate defectiveness insource code. However, the existing methods are dependent on experts orinefficient. In this paper, we improve the performance (in terms of speed andmemory usage) of our existing code review assisting tool--CRUSO. The centralidea of the approach is to estimate the defectiveness for an input source codeby using the defectiveness score of similar code fragments present in variousStackOverflow (SO) posts.  The significant contributions of our paper are i) SOpostsDB: a datasetcontaining the PVA vectors and the SO posts information, ii) CRUSO-P: a codereview assisting system based on PVA models trained on \emph{SOpostsDB}. For agiven input source code, CRUSO-P labels it as {Likely to be defective, Unlikelyto be defective, Unpredictable}. To develop CRUSO-P, we processed >3 million SOposts and 188200+ GitHub source files. CRUSO-P is designed to work with sourcecode written in the popular programming languages {C, C#, Java, JavaScript, andPython}.  CRUSO-P outperforms CRUSO with an improvement of 97.82% in response time anda storage reduction of 99.15%. CRUSO-P achieves the highest mean accuracy scoreof 99.6% when tested with the C programming language, thus achieving animprovement of 5.6% over the existing method.

Using Paragraph Vectors to improve our existing code review assisting tool-CRUSO