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作者： Muhammed Yasin Bahtiyar Linnaeus University

学位级别：硕士

An unavoidable amount of money is spent on maintaining existing software systems today. Software maintenance cost generally higher than development cost of the system therefore lowering maintenance cost is highly appreciated in software industry. A significant part of maintenance activities is related to repeating the investigation of problems and applying repeated solutions several times. A software system may contain a common bug in several different places and it might take extra effort and time to fix all existences of this bug. This operation commonly increases the cost of Software Maintenance Activities. Detecting duplicate code fragments can significantly decrease the time and effort therefore the maintenance cost. Clone code detection can be achieved via analyzing the source code of given software system. An abstract syntax tree based clone detector for java systems is designed and implemented through this study. This master thesis examines a software engineering process to create an abstract syntax tree based clone detector for the projects implemented in Java programming language.

关键词： clone detection abstract syntax tree java eclipse software maintenance

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Generic,efficient,and effective deobfuscation and semantic-aware attack detection for Power Shell scripts

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Frontiers of Information Technology & Electronic Engineering 2022年第3期23卷 361-381页

作者： Chunlin XIONG Zhenyuan LI Yan CHEN Tiantian ZHU Jian WANG Hai YANG Wei RUAN College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Department of Electrical Engineering and Computer Science Northwestern UniversityEvanstonIL 60208USA College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China Magic Shield Co. Ltd.Hangzhou 310027China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

In recent years,Power Shell has increasingly been reported as appearing in a variety of cyber ***,because the PowerShell language is dynamic by design and can construct script fragments at different levels,state-of-the-art static analysis based Power Shell attack detection approaches are inherently vulnerable to *** this paper,we design the first generic,effective,and lightweight deobfuscation approach for PowerShell *** precisely identify the obfuscated script fragments,we define obfuscation based on the differences in the impacts on the abstract syntax trees of PowerShell scripts and propose a novel emulation-based recovery ***,we design the first semantic-aware PowerShell attack detection system that leverages the classic objective-oriented association mining algorithm and newly identifies 31 semantic *** experimental results on 2342 benign samples and 4141 malicious samples show that our deobfuscation method takes less than 0.5 s on average and increases the similarity between the obfuscated and original scripts from 0.5%to 93.2%.By deploying our deobfuscation method,the attack detection rates for Windows Defender and VirusTotal increase substantially from 0.33%and 2.65%to 78.9%and 94.0%,***,our detection system outperforms both existing tools with a 96.7%true positive rate and a 0%false positive rate on average.

关键词： PowerShell abstract syntax tree Obfuscation and deobfuscation Malicious script detection

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IntelliSense Implementation of a Dynamic Language

IntelliSense Implementation of a Dynamic Language

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作者： Míšek, Jakub Charles University of Prague

In the context of computer programming, the importance of computer assistance is being understood by many developer communities. Developers are e.g. using the same well known expressions or searching method signatures in library documentations. Code sense or IntelliSense methods make most of these actions unnecessary because they serve the available useful information directly to the programmer in a completely automated way. Recently, with the increased focus of the industry on dynamic languages a problem emerges - the complete knowledge on the source code is postponed until the runtime, since there may be ambiguous semantics in the code fragment. As a part of the Phalanger project the methods for syntax and semantic analysis of the dynamic code were designed, especially targeted for the PHP programming language. These methods produce a list of valid possibilities which can be then used on a specified position in the source code; such as declarations, variables and function parameters. This collected information can be also used to a fine-grained syntax highlighting.

关键词： IntelliSense PHP dynamic language Phalanger Code Analysis abstract syntax tree

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Analyzing syntactic constructs of Java programs with machine learning

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Ortin, Francisco Facundo, Guillermo Garcia, Miguel Univ Oviedo Comp Sci Dept C Federico Garcia Lorca 18 Oviedo 33007 Spain Munster Technol Univ Comp Sci Dept Rossa Ave Cork Ireland

The massive number of open-source projects in public repositories has notably increased in the last years. Such repositories represent valuable information to be mined for different purposes, such as documenting recurrent syntactic constructs, analyzing the particular constructs used by experts and beginners, using them to teach programming and to detect bad programming practices, and building programming tools such as decompilers, Integrated Development Environments or Intelligent Tutoring Systems. An inherent problem of source code is that its syntactic information is represented with tree structures, while traditional machine learning algorithms use n-dimensional datasets. Therefore, we present a feature engineering process to translate tree structures into homogeneous and heterogeneous n-dimensional datasets to be mined. Then, we run different interpretable (supervised and unsupervised) machine learning algorithms to mine the syntactic information of more than 17 million syntactic constructs in Java code. The results reveal interesting information such as the Java constructs that are barely (and widely) used (e.g., bitwise operators, union types and static blocks), different language features and patterns mostly (and barely) used by beginners (and experts), the discovery of particular types of source code (e.g., helper or utility classes, data transfer objects and too complex abstractions), and how complexity is an inherent characteristic in some clusters of syntactic constructs.

关键词： abstract syntax tree Programming language Data mining Feature engineering Programming idiom Heterogeneous dataset

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Using expression parsing and algebraic operations to generate test sequences.✩

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JOURNAL OF SYSTEMS AND SOFTWARE 2023年第1期204卷

作者： Liu, Pan Li, Yihao Ludong Univ Sch Informat & Elect Engn Yantai Peoples R China Shanghai Business Sch Fac Business Informat Shanghai Peoples R China

It has become a popular trend to build software's regular expression or extended regular expression models in order to generate test sequences from these models. Such test sequences tend to have promising test coverage and fault detection capability. During this process, one critical step is expression parsing based on algebraic operations. However, the parsing can be very challenging as different algebraic systems have different algebraic operators and algebraic operations. Besides, the parsing difficulty continues to grow as software complexity increases. To address the above challenges, this paper proposes a general expression parsing framework for test sequence generation. The proposed framework consists of three stages, expression decomposition, algebraic operations, and subexpression combination. To implement the framework, an expression parsing algorithm based on abstract syntax tree is developed. Case studies based on 117 expressions collected from the literature over the past 30 years as well as 13 software systems are conducted to evaluate the effectiveness of the proposed algorithm. The results indicate that our algorithm is superior to three existing and commonly used algorithms with respect to expression parsing and software fault detection. & COPY;2023 Elsevier Inc. All rights reserved.

关键词： Expression parsing Test sequence generation Algebraic operation abstract syntax tree

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GRRLN: Gated Recurrent Residual Learning Networks for code clone detection

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JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS 2024年第7期36卷

作者： Zhang, Xiangping Liu, Jianxun Shi, Min Hunan Automot Engn Vocat Coll Zhuzhou Hunan Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Hunan Peoples R China Hunan Univ Sci & Technol Hunan Prov Key Lab Serv Comp & Novel Software Tech Xiangtan Hunan Peoples R China Harvard Med Sch Harvard Ophthalmol AI Lab Boston MA USA

Code clone detection is a critical problem in software development and maintenance domains. It aims to identify functionally identical or similar code fragments within an application. Existing works formulate the code clone detection task as a binary classification problem which predicts a code pair as a clone or not based on a pre-defined threshold. In reality, there are various types of code clone subject to the degree of how a pair of code fragments are similar to each other. To investigate the effect of different code clone detection manners on the clone detection result, we propose Gated Recurrent Residual Learning Networks (GRRLN), a novel neural network model for code clone detection. To train GRRLN, we first represent each code fragment as a statement-level tree sequence derived from the whole abstract syntax tree (AST). Then, a gated recurrent neural network with residual connections is adopted to fully extract the semantics of all individual statement trees together with their dependency relationships across the input statement sequence. Finally, the output representations of code fragments by GRRLN are used for similarity calculation and clone detection. We evaluate GRRLN using two real-world datasets for code clone detection and clone type classification. Experiments show that GRRLN achieves promising and compelling results and meanwhile needs significantly less time and memory consumption compared with the state-of-the-art methods. Code clone detection is commonly approached as a binary classification task, determining whether code pairs are clones or not based on a fixed threshold. However, code clones exhibit varying degrees of similarity, leading to different types of clones. To explore the impact of detection manners on clone detection results, we proposed a Gated Recurrent Residual Learning Networks for code clone detection task. The experimental results demonstrate that different detection manners yield varying results, even with the same model an

关键词： abstract syntax tree code clone detection code representation residual network

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On the impact of multiple source code representations on software engineering tasks - An empirical study

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 210卷

作者： Swarna, Karthik Chandra Mathews, Noble Saji Vagavolu, Dheeraj Chimalakonda, Sridhar Indian Inst Technol Tirupati Dept Comp Sci & Engn Res Intelligent Software & Human Analyt RISHA Lab Tirupati India

Efficiently representing source code is crucial for various software engineering tasks such as code classification and clone detection. Existing approaches primarily use abstract syntax tree (AST), and only a few focus on semantic graphs such as Control Flow Graph (CFG) and Program Dependency Graph (PDG), which contain information about source code that AST does not. Even though some works tried to utilize multiple representations, they do not provide any insights about the costs and benefits of using multiple representations. The primary goal of this paper is to discuss the implications of utilizing multiple source code representations, specifically AST, CFG, and PDG. We modify an AST path -based approach to accept multiple representations as input to an attention -based model. We do this to measure the impact of additional representations (such as CFG and PDG) over AST. We evaluate our approach on three tasks: Method Naming, Program Classification, and Clone Detection. Our approach increases the performance on these tasks by 11% (F1), 15.7% (Accuracy), and 9.3% (F1), respectively, over the baseline. In addition to the effect on performance, we discuss timing overheads incurred with multiple representations. We envision that this work can provide a base for researchers to explore and experiment with a variety of source code representations for software engineering tasks.

关键词： Source code representation abstract syntax tree Control Flow Graph Program Dependence Graph Code embedding Method naming

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Fast Code Clone Detection Based on Weighted Recursive Autoencoders

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IEEE ACCESS 2019年 7卷 125062-125078页

作者： Zeng, Jie Ben, Kerong Li, Xiaowei Zhang, Xian Naval Univ Engn Coll Elect Engn Wuhan 430033 Hubei Peoples R China Jinghang Res Inst Comp & Commun Beijing 100074 Peoples R China

Code cloning considerably facilitates software development but also leads to recurring bugs and other software quality problems. In this paper, we propose a fast code clone detection method based on weighted recursive autoencoders (RAE) to measure code similarity at the function level. Different from manually defining features for code clone detection, our deep learning-based method can automatically learn program features. First, we analyze program abstract syntax trees using weighted RAE, extract the program features and encode the functions to vectors. During the modeling process, we consider node weight information in abstract syntax trees to increase the proportion of information contributed by important nodes in the final vector representation of one program. Second, we report functions with similar vectors as code clone pairs. The second phase is time consuming when analyzing large software systems because it needs quadratic pairwise comparisons. To solve this problem, we transform the clone detection problem into an approximate nearest neighbors search (ANNS) in a high-dimensional vector set and use the navigating spreading-out graph to reduce the computational time complexity. Experimental results on BigCloneBench show that our method outperforms the compared algorithm based on unweighted RAE in terms of precision, recall, and AUC value and can return clone pairs in approximately 33 min, while the compared algorithm requires approximately 14 days when performing pairwise comparisons among 785,438 functions' vectors. Our method also outperforms many prominent tools, including Oreo, in detecting Moderately Type-3 or Type-4 clones, and our false positive rate (FPR) equals 0.055, which means few false positives. More importantly, our method has no need for labeled data, and all of the source code is released to guarantee experimental reproducibility.

关键词： Code clone fast code clone detection abstract syntax tree weighted recursive autoen-coders navigating spreading-out graph

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Just-in-time defect prediction based on AST change embedding

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KNOWLEDGE-BASED SYSTEMS 2022年 248卷 1页

作者： Zhuang, Weiyuan Wang, Hao Zhang, Xiaofang Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

Just-in-time (JIT) defect prediction can help developers quickly identify whether a change is defective or not. The features extracted from changes play an essential role in building an accurate prediction model. In recent years, it has been considered effective to extract the semantic features of software code files by using code representation technology. However, how to extract semantic information from broken changing code snippets is still a challenging problem. We propose a new feature to represent code semantics based on abstract syntax trees (ASTs), called ACE (AST Change Embedding), by comparing the abstract syntax tree of source code before and after a change and extracting AST change sequences, and then mapping them into numeric vectors by using word embedding technology. At the same time, we utilize the gated mechanism to build a gated hierarchical model, called GH-ACE, to combine the traditional manual features and semantic features. We conduct experiments on within-project and cross-project defect prediction tasks and evaluate the effectiveness of our proposed model in non-effort-aware scenarios and effort-aware scenarios. The results show that, on average, our model is 4.0 percent higher than the best baseline method for within-project defect prediction and 2.4 percent higher than the best baseline method for cross-project defect prediction. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Just-in-time defect prediction Code representation abstract syntax tree Word embedding

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Generování backendu aplikace z UML modelu

Generování backendu aplikace z UML modelu

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作者： Klikar, Václav Brno University of Technology

Cílem této bakalářské práce je navrhnout a implementovat nástroj umožňující obousměrnou synchronizaci UML diagramu tříd a backendu aplikace kdykoliv v průběhu tvorby apl... 详细信息

Cílem této bakalářské práce je navrhnout a implementovat nástroj umožňující obousměrnou synchronizaci UML diagramu tříd a backendu aplikace kdykoliv v průběhu tvorby aplikace. Práce s UML modelem je umožněna pomocí standardizovaného XMI formátu. Backend aplikace je vytvořen a spravován prostřednictvím webového rámce Django. Pro čtení a zápis programového kódu Djanga je využit syntaktický analyzátor abstraktních syntaktických stromů.

关键词： Backend administrace webový rámec Django UML diagram tříd XMI PySide abstraktní syntaktický strom Elementtree generování kódu synchronizace. Backend administration web framework Django UML class diagram XMI PySide abstract syntax tree Ele

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