409 research outputs found
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes
We propose a model to automatically describe changes introduced in the source
code of a program using natural language. Our method receives as input a set of
code commits, which contains both the modifications and message introduced by
an user. These two modalities are used to train an encoder-decoder
architecture. We evaluated our approach on twelve real world open source
projects from four different programming languages. Quantitative and
qualitative results showed that the proposed approach can generate feasible and
semantically sound descriptions not only in standard in-project settings, but
also in a cross-project setting.Comment: Accepted at ACL 201
Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs
Binary code analysis allows analyzing binary code without having access to
the corresponding source code. A binary, after disassembly, is expressed in an
assembly language. This inspires us to approach binary analysis by leveraging
ideas and techniques from Natural Language Processing (NLP), a rich area
focused on processing text of various natural languages. We notice that binary
code analysis and NLP share a lot of analogical topics, such as semantics
extraction, summarization, and classification. This work utilizes these ideas
to address two important code similarity comparison problems. (I) Given a pair
of basic blocks for different instruction set architectures (ISAs), determining
whether their semantics is similar or not; and (II) given a piece of code of
interest, determining if it is contained in another piece of assembly code for
a different ISA. The solutions to these two problems have many applications,
such as cross-architecture vulnerability discovery and code plagiarism
detection. We implement a prototype system INNEREYE and perform a comprehensive
evaluation. A comparison between our approach and existing approaches to
Problem I shows that our system outperforms them in terms of accuracy,
efficiency and scalability. And the case studies utilizing the system
demonstrate that our solution to Problem II is effective. Moreover, this
research showcases how to apply ideas and techniques from NLP to large-scale
binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium
201
Learning representations for effective and explainable software bug detection and fixing
Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a \u27bridge,\u27 known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning.
This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning code representations using four distinct methods?context-based, testing results-based, tree-based, and graph-based?thus improving bug detection and fixing approaches, as well as providing developers insight into the foundational reasoning. The experimental results demonstrate that using learning code representations can significantly enhance explainable bug detection and fixing, showcasing the practicability and meaningfulness of the approaches formulated in this dissertation toward improving software quality and reliability
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