Source code summarization refers to the natural language description of the source code’s function. It can help developers easily understand the semantics of the source code. We can think of the source code and the corresponding summarization as being symmetric. However, the existing source code summarization is mismatched with the source code, missing, or out of date. Manual source code summarization is inefficient and requires a lot of human efforts. To overcome such situations, many studies have been conducted on Automatic Source Code Summarization (ASCS). Given a set of source code, the ASCS techniques can automatically generate a summary described with natural language. In this paper, we give a review of the development of ASCS technology. Almost all ASCS technology involves the following stages: source code modeling, code summarization generation, and quality evaluation. We further categorize the existing ASCS techniques based on the above stages and analyze their advantages and shortcomings. We also draw a clear map on the development of the existing algorithms.

Currently, among the millions of Android applications, there exist numerous malicious programs that pose significant threats to people’s security and privacy. Therefore, it is imperative to develop approaches for detecting Android malware. Recently developed malware detection methods usually rely on various features, such as application programming interface (API) sequences, images, and permissions, thereby ignoring the importance of source code and the associated comments, which are not usually included in malware. Therefore, we propose Android-SEM, which is an Android source code semantic enhancement model based on transfer learning. Our proposed model is built upon the Transformer architecture to achieve a pretraining framework for generating code comments from malware source code. The performance of the pretraining framework is optimized using a generative adversarial network. Our proposed model relies on a novel regression model-based filter to retain high-quality comments and source code for feature fusion pertinent to semantic enhancement. Creatively, and contrary to conventional methods, we incorporated a quantum support vector machine (QSVM) for classifying malicious Android code by combining quantum machine learning and classical deep learning models. The results proved that Android-SEM achieves accuracy levels of 99.55% and 99.01% for malware detection and malware categorization, respectively.

Source code summarization (SCS) is a natural language description of source code functionality. It can help developers understand programs and maintain software efficiently. Retrieval-based methods generate SCS by reorganizing terms selected from source code or use SCS of similar code snippets. Generative methods generate SCS via attentional encoder–decoder architecture. However, a generative method can generate SCS for any code, but sometimes the accuracy is still far from expectation (due to the lack of numerous high-quality training sets). A retrieval-based method is considered to have a higher accurac, but usually fails to generate SCS for a source code in the absence of a similar candidate in the database. In order to effectively combine the advantages of retrieval-based methods and generative methods, we propose a new method: Re_Trans. For a given code, we first utilize the retrieval-based method to obtain its most similar code with regard to sematic and corresponding SCS (S_RM). Then, we input the given code and similar code into the trained discriminator. If the discriminator outputs onr, we take S_RM as the result; otherwise, we utilize the generate model, transformer, to generate the given code’ SCS. Particularly, we use AST-augmented (AbstractSyntax Tree) and code sequence-augmented information to make the source code semantic extraction more complete. Furthermore, we build a new SCS retrieval library through the public dataset. We evaluate our method on a dataset of 2.1 million Java code-comment pairs, and experimental results show improvement over the state-of-the-art (SOTA) benchmarks, which demonstrates the effectiveness and efficiency of our method.

Automatic malware detection was aimed at determining whether the application is malicious or not with automated systems. Android malware attacks have gained tremendous pace owing to the widespread use of mobile devices. Although significant progress has been made in antimalware techniques, these methods mainly rely on the program features, ignoring the importance of source code analysis. Furthermore, the dynamic analysis is low code coverage and poor efficiency. Hence, we propose an automatic Android malware detection approach, named HyGNN‐Mal. It analyzes the Android applications at source code level by exploiting the sequence and structure information. Meanwhile, we combine the typical static features, permissions, and APIs. In HyGNN‐Mal, we utilize a deep traversal tree neural network (Deep‐TNN) to process the code structure information. Particularly, we add position information to code sequence information before putting in self‐attention mechanism. The evaluations conducted on multiple public datasets indicate that our method can accurately identify and classify the malicious software, and their best accuracy is 99.62% and 99.2%, respectively.

With the rapid development of information technology, the scale of software has increased exponentially. Binary code similarity detection technology plays an important role in many fields, such as detecting software plagiarism, vulnerabilities discovery, and copyright solution issues. Nevertheless, what cannot be ignored is that a variety of approaches to binary code semantic representation have been introduced recently, but few can catch up with existing code obfuscation techniques due to their maturing and extensive development. In order to solve this problem, we propose a new neural network model, named SROBR, which is a deep integration of natural language processing model and graph neural network. In SROBR, BERT is applied to capture sequence information of the binary code at the first place, and then GAT is utilized to capture the structural information. It combines natural language processing and graph neural network, which can capture the semantic information of binary programs while resisting obfuscation options in a more efficient way. Through binary code similarity detection task and obfuscated option classification task, the experimental results demonstrate that SROBR outperforms existing binary similarity detection methods in resisting obfuscation techniques.

With the improvement of software copyright protection awareness, code obfuscation technology plays a crucial role in protecting key code segments. As the obfuscation technology becomes more and more complex and diverse, it has spawned a large number of malware variants, which make it easy to evade the detection of anti-virus software. Malicious code detection mainly depends on binary code similarity analysis. However, the existing software analysis technologies are difficult to deal with the growing complex obfuscation technologies. To solve this problem, this paper proposes a new obfuscation-resilient program analysis method, which is based on the data flow transformation relationship of the intermediate representation and the graph network model. In our approach, we first construct the data transformation graph based on LLVM IR. Then, we design a novel intermediate language representation model based on graph networks, named DFSGraph, to learn the data flow semantics from DTG. DFSGraph can detect the similarity of obfuscated code by extracting the semantic information of program data flow without deobfuscation. Extensive experiments prove that our approach is more accurate than existing deobfuscation tools when searching for similar functions from obfuscated code.