Building Program Vector Representations for Deep Learning

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.Comment: This paper was submitted to ICSE'1

Convolutional Neural Networks over Tree Structures for Programming Language Processing

Programming language processing (similar to natural language processing) is a hot research topic in the field of software engineering; it has also aroused growing interest in the artificial intelligence community. However, different from a natural language sentence, a program contains rich, explicit, and complicated structural information. Hence, traditional NLP models may be inappropriate for programs. In this paper, we propose a novel tree-based convolutional neural network (TBCNN) for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. TBCNN is a generic architecture for programming language processing; our experiments show its effectiveness in two different program analysis tasks: classifying programs according to functionality, and detecting code snippets of certain patterns. TBCNN outperforms baseline methods, including several neural models for NLP.Comment: Accepted at AAAI-1

Universal Dependencies Parsing for Colloquial Singaporean English

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.Comment: Accepted by ACL 201

Static Analysis for Discovering Security Vulnerabilities in Web Applications on the Asp.Net Platform

Tato bakalářská práce popisuje jak teoretické základy, tak způsob vytvoření statického analyzátoru založeném na platformě .NET Framework a službách poskytnutých prostřednictvím .NET Compiler Platform. Tento analyzátor detekuje bezpečnostní slabiny typu SQL injection na platformě ASP.NET MVC. Analyzátor nejdříve sestrojuje grafy řízení toku jako abstraktní reprezentaci analyzovaného programu. Poté využívá statické analýzy pro sledování potenciálně nedůvěryhodných dat. Nakonec jsou výsledky analýzy prezentovány uživateli.This Bachelor thesis is intended to describe theoretical foundations as well as the construction of a static taint analyser based on the .NET Framework and the analysis services provided by the .NET Compiler Platform. This analyser detects SQL injection security vulnerabilities on the ASP.NET MVC platform. Firstly, the analyser constructs control flow graphs as an abstract representation of the analysed program. Then, it uses a static taint analysis to track potentially distrusted and tainted data values. Finally, analysis results are presented to the user.