modification of control structures, modification of data structures or structural redesign of source code) the source code can be changed in such a way that it almost looks genuine. For example, by some structural modifications (e.g. Those systems need to recognize a number of lexical and structural source code modifications. Various source code similarity detection systems have been developed to help detect source code plagiarism. Source code plagiarism is an easy to do task, but very difficult to detect without proper tool support. Interestingly, using our parsimonious rule-based model to assess the sentiment of tweets, we find that VADER outperforms individual human raters (F1 Classification Accuracy = 0.96 and 0.84, respectively), and generalizes more favorably across contexts than any of our benchmarks. We then combine these lexical features with consideration for five general rules that embody grammatical and syntactical conventions for expressing and emphasizing sentiment intensity. Using a combination of qualitative and quantitative methods, we first construct and empirically validate a gold-standard list of lexical features (along with their associated sentiment intensity measures) which are specifically attuned to sentiment in microblog-like contexts.
We present VADER, a simple rule-based model for general sentiment analysis, and compare its effectiveness to eleven typical state-of-practice benchmarks including LIWC, ANEW, the General Inquirer, SentiWordNet, and machine learning oriented techniques relying on Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM) algorithms. The inherent nature of social media content poses serious challenges to practical applications of sentiment analysis. A precision of more than 90% in identifying correct/relevant results has been achieved. In the experiments that we performed to verify effectiveness of our approach source code samples from more than 300 GitHub open source repositories were taken as input. Our choice of document fingerprinting technique is inspired by source code plagiarism detection tools where it has proven to be very successful. A novel aspect of our approach is to use document fingerprinting for comparing two pieces of source code. b) Determine the likelihood of C being defective by considering all p in P. Basic idea employed in the tool is to: a) Identify a set P of discussion posts on StackOverflow such that each p in P contains source code fragment(s) which sufficiently resemble the input code C being reviewed. Ii) CRUSO-P: a code review assisting system based on PVA models trained on SOpostsDB.įor a given input source code, CRUSO-P labels it as. The significant contributions of our paper are i) SOpostsDB: a dataset containing the PVA vectors and the SO posts information, The central idea of the approach is to estimate the defectiveness for an input source code by using the defectiveness score of similar code fragments present in various StackOverflow (SO) posts.
In this paper, we improve the performance (in terms of speed and memory usage) of our existing code review assisting tool-CRUSO. However, the existing methods are dependent on experts or inefficient. Code reviews are one of the effective methods to estimate defectiveness in source code.