Biblio

Style transfer is an emerging trend in the fields of deep learning's applications, especially in images and audio data this is proven very useful and sometimes the results are astonishing. Gradually styles of textual data are also being changed in many novel works. This paper focuses on the transfer of the sentimental vibe of a sentence. Given a positive clause, the negative version of that clause or sentence is generated keeping the context same. The opposite is also done with negative sentences. Previously this was a very tough job because the go-to techniques for such tasks such as Recurrent Neural Networks (RNNs) [1] and Long Short-Term Memories(LSTMs) [2] can't perform well with it. But since newer technologies like Generative Adversarial Network(GAN) and Variational AutoEncoder(VAE) are emerging, this work seem to become more and more possible and effective. In this paper, Multi-Genarative Variational Auto-Encoder is employed to transfer sentiment values. Inspite of working with a small dataset, this model proves to be promising.

The large scale and high complexity of modern software systems make perfectly precise static code analysis (SCA) infeasible. Therefore SCA tools often over-approximate, so not to miss any real problems. This, however, comes at the expense of raising false alarms, which, in practice, reduces the usability of these tools. To partially address this problem, we propose a novel learning process whose goal is to discover program structures that cause a given SCA tool to emit false error reports, and then to use this information to predict whether a new error report is likely to be a false positive as well. To do this, we first preprocess code to isolate the locations that are related to the error report. Then, we apply machine learning techniques to the preprocessed code to discover correlations and to learn a classifier. We evaluated this approach in an initial case study of a widely-used SCA tool for Java. Our results showed that for our dataset we could accurately classify a large majority of false positive error reports. Moreover, we identified some common coding patterns that led to false positive errors. We believe that SCA developers may be able to redesign their methods to address these patterns and reduce false positive error reports.