A comparison of code similarity analysers

Copying and pasting of source code is a common activity in software engineering. Often, the code is not copied as it is and it may be modified for various purposes; e.g. refactoring, bug fixing, or even software plagiarism. These code modifications could affect the performance of code similarity analysers including code clone and plagiarism detectors to some certain degree. We are interested in two types of code modification in this study: pervasive modifications, i.e. transformations that may have a global effect, and local modifications, i.e. code changes that are contained in a single method or code block. We evaluate 30 code similarity detection techniques and tools using five experimental scenarios for Java source code. These are (1) pervasively modified code, created with tools for source code and bytecode obfuscation, and boiler-plate code, (2) source code normalisation through compilation and decompilation using different decompilers, (3) reuse of optimal configurations over different data sets, (4) tool evaluation using ranked-based measures, and (5) local + global code modifications. Our experimental results show that in the presence of pervasive modifications, some of the general textual similarity measures can offer similar performance to specialised code similarity tools, whilst in the presence of boiler-plate code, highly specialised source code similarity detection techniques and tools outperform textual similarity measures. Our study strongly validates the use of compilation/decompilation as a normalisation technique. Its use reduced false classifications to zero for three of the tools. Moreover, we demonstrate that optimal configurations are very sensitive to a specific data set. After directly applying optimal configurations derived from one data set to another, the tools perform poorly on the new data set. The code similarity analysers are thoroughly evaluated not only based on several well-known pair-based and query-based error measures but also on each specific type of pervasive code modification. This broad, thorough study is the largest in existence and potentially an invaluable guide for future users of similarity detection in source code

A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.Comment: 49 pages, 10 figures, 6 table

Android application forensics: A survey of obfuscation, obfuscation detection and deobfuscation techniques and their impact on investigations

Android obfuscation techniques include not only classic code obfuscation techniques that were adapted to Android, but also obfuscation methods that target the Android platform specifically. This work examines the status-quo of Android obfuscation, obfuscation detection and deobfuscation. Specifically, it first summarizes obfuscation approaches that are commonly used by app developers for code optimization, to protect their software against code theft and code tampering but are also frequently misused by malware developers to circumvent anti-malware products. Secondly, the article focuses on obfuscation detection techniques and presents various available tools and current research. Thirdly, deobfuscation (which aims at reinstating the original state before obfuscation) is discussed followed by a brief discussion how this impacts forensic investigation. We conclude that although obfuscation is widely used in Android app development (benign and malicious), available tools and the practices on how to deal with obfuscation are not standardized, and so are inherently lacking from a forensic standpoint

Towards Semantic Clone Detection, Benchmarking, and Evaluation

Developers copy and paste their code to speed up the development process. Sometimes, they copy code from other systems or look up code online to solve a complex problem. Developers reuse copied code with or without modifications. The resulting similar or identical code fragments are called code clones. Sometimes clones are unintentionally written when a developer implements the same or similar functionality. Even when the resulting code fragments are not textually similar but implement the same functionality they are still considered to be clones and are classified as semantic clones. Semantic clones are defined as code fragments that perform the exact same computation and are implemented using different syntax. Software cloning research indicates that code clones exist in all software systems; on average, 5% to 20% of software code is cloned. Due to the potential impact of clones, whether positive or negative, it is essential to locate, track, and manage clones in the source code. Considerable research has been conducted on all types of code clones, including clone detection, analysis, management, and evaluation. Despite the great interest in code clones, there has been considerably less work conducted on semantic clones. As described in this thesis, I advance the state-of-the-art in semantic clone research in several ways. First, I conducted an empirical study to investigate the status of code cloning in and across open-source game systems and the effectiveness of different normalization, filtering, and transformation techniques for detecting semantic clones. Second, I developed an approach to detect clones across .NET programming languages using an intermediate language. Third, I developed a technique using an intermediate language and an ontology to detect semantic clones. Fourth, I mined Stack Overflow answers to build a semantic code clone benchmark that represents real semantic code clones in four programming languages, C, C#, Java, and Python. Fifth, I defined a comprehensive taxonomy that identifies semantic clone types. Finally, I implemented an injection framework that uses the benchmark to compare and evaluate semantic code clone detectors by automatically measuring recall

Code similarity and clone search in large-scale source code data

Software development is tremendously benefited from the Internet by having online code corpora that enable instant sharing of source code and online developer's guides and documentation. Nowadays, duplicated code (i.e., code clones) not only exists within or across software projects but also between online code repositories and websites. We call them "online code clones."' They can lead to license violations, bug propagation, and re-use of outdated code similar to classic code clones between software systems. Unfortunately, they are difficult to locate and fix since the search space in online code corpora is large and no longer confined to a local repository. This thesis presents a combined study of code similarity and online code clones. We empirically show that many code snippets on Stack Overflow are cloned from open source projects. Several of them become outdated or violate their original license and are possibly harmful to reuse. To develop a solution for finding online code clones, we study various code similarity techniques to gain insights into their strengths and weaknesses. A framework, called OCD, for evaluating code similarity and clone search tools is introduced and used to compare 34 state-of-the-art techniques on pervasively modified code and boiler-plate code. We also found that clone detection techniques can be enhanced by compilation and decompilation. Using the knowledge from the comparison of code similarity analysers, we create and evaluate Siamese, a scalable token-based clone search technique via multiple code representations. Our evaluation shows that Siamese scales to large-scale source code data of 365 million lines of code and offers high search precision and recall. Its clone search precision is comparable to seven state-of-the-art clone detection tools on the OCD framework. Finally, we demonstrate the usefulness of Siamese by applying the tool to find online code clones, automatically analyse clone licenses, and recommend tests for reuse

Codeklonerkennung mit Dominatorinformationen

If an existing function in a software project is copied and reused (in a slightly modified version), the result is a code clone. If there was an error or vulnerability in the original function, this error or vulnerability is now contained in several places in the software project. This is one of the reasons why research is being done to develop powerful and scalable clone detection techniques. In this thesis, a new clone detection method is presented that uses paths and path sets derived from the dominator trees of the functions to detect the code clones. A dominator tree is a special form of the control flow graph, which does not contain cycles. The dominator tree based method has been implemented in the StoneDetector tool and can detect code clones in Java source code as well as in Java bytecode. It has equally good or better recall and precision results than previously published code clone detection methods. The evaluation was performed using the BigCloneBench. Scalability measurements showed that even source code with several 100 million lines of code can be searched in a reasonable time. In order to evaluate the bytecode based StoneDetector variant, the BigCloneBench files had to be compiled. For this purpose, the Stubber tool was developed, which can compile Java source code files without the required libraries. Finally, it could be shown that using the register code generated from the Java bytecode, similar recall and precision values could be achieved compared to the source code based variant. Since some machine learning studies specify that very good recall and precision values can be achieved for all clone types, a machine learning method was trained with dominator trees. It could be shown that the results published by the studies are not reproducible on unseen data.Wird eine bestehende Funktion in einem Softwareprojekt kopiert und (in leicht angepasster Form) erneut genutzt, entsteht ein Codeklon. War in der ursprünglichen Funktion jedoch ein Fehler oder eine Schwachstelle, so ist dieser Fehler beziehungsweise diese Schwachstelle jetzt an mehreren Stellen im Softwareprojekt enthalten. Dies ist einer der Gründe, weshalb an der Entwicklung von leistungsstarken und skalierbaren Klonerkennungsverfahren geforscht wird. In der hier vorliegenden Arbeit wird ein neues Klonerkennungsverfahren vorgestellt, das zum Detektieren der Codeklone Pfade und Pfadmengen nutzt, die aus den Dominatorbäumen der Funktionen abgeleitet werden. Ein Dominatorbaum wird aus dem Kontrollflussgraphen abgeleitet und enthält keine Zyklen. Das Dominatorbaum-basierte Verfahren wurde in dem Werkzeug StoneDetector umgesetzt und kann Codeklone sowohl im Java-Quelltext als auch im Java-Bytecode detektieren. Dabei hat es gleich gute oder bessere Recall- und Precision-Werte als bisher veröffentlichte Codeklonerkennungsverfahren. Die Wert-Evaluierungen wurden dabei unter Verwendung des BigClone-Benchs durchgeführt. Skalierbarkeitsmessungen zeigten, dass sogar Quellcodedateien mit mehreren 100-Millionen Codezeilen in angemessener Zeit durchsucht werden können. Damit die Bytecode-basierte StoneDetector-Variante auch evaluiert werden konnte, mussten die Dateien des BigCloneBench kompiliert werden. Dazu wurde das Stubber-Tool entwickelt, welches Java-Quelltextdateien ohne die benötigten Abhängigkeiten kompilieren kann. Schlussendlich konnte somit gezeigt werden, dass mithilfe des aus dem Java-Bytecode generierten Registercodes ähnliche Recall- und Precision-Werte im Vergleich zu der Quelltext-basierten Variante erreicht werden können. Da einige Arbeiten mit maschinellen Lernverfahren angeben, bei allen Klontypen sehr gute Recall- und Precision-Werte zu erreichen, wurde ein maschinelles Lernverfahren mit Dominatoräumen trainiert. Es konnte gezeigt werden, dass die von den Arbeiten veröffentlichten Ergebnisse nicht auf ungesehenen Daten reproduzierbar sind

Management Aspects of Software Clone Detection and Analysis

Copying a code fragment and reusing it by pasting with or without minor modifications is a common practice in software development for improved productivity. As a result, software systems often have similar segments of code, called software clones or code clones. Due to many reasons, unintentional clones may also appear in the source code without awareness of the developer. Studies report that significant fractions (5% to 50%) of the code in typical software systems are cloned. Although code cloning may increase initial productivity, it may cause fault propagation, inflate the code base and increase maintenance overhead. Thus, it is believed that code clones should be identified and carefully managed. This Ph.D. thesis contributes in clone management with techniques realized into tools and large-scale in-depth analyses of clones to inform clone management in devising effective techniques and strategies. To support proactive clone management, we have developed a clone detector as a plug-in to the Eclipse IDE. For clone detection, we used a hybrid approach that combines the strength of both parser-based and text-based techniques. To capture clones that are similar but not exact duplicates, we adopted a novel approach that applies a suffix-tree-based k-difference hybrid algorithm, borrowed from the area of computational biology. Instead of targeting all clones from the entire code base, our tool aids clone-aware development by allowing focused search for clones of any code fragment of the developer's interest. A good understanding on the code cloning phenomenon is a prerequisite to devise efficient clone management strategies. The second phase of the thesis includes large-scale empirical studies on the characteristics (e.g., proportion, types of similarity, change patterns) of code clones in evolving software systems. Applying statistical techniques, we also made fairly accurate forecast on the proportion of code clones in the future versions of software projects. The outcome of these studies expose useful insights into the characteristics of evolving clones and their management implications. Upon identification of the code clones, their management often necessitates careful refactoring, which is dealt with at the third phase of the thesis. Given a large number of clones, it is difficult to optimally decide what to refactor and what not, especially when there are dependencies among clones and the objective remains the minimization of refactoring efforts and risks while maximizing benefits. In this regard, we developed a novel clone refactoring scheduler that applies a constraint programming approach. We also introduced a novel effort model for the estimation of efforts needed to refactor clones in source code. We evaluated our clone detector, scheduler and effort model through comparative empirical studies and user studies. Finally, based on our experience and in-depth analysis of the present state of the art, we expose avenues for further research and development towards a versatile clone management system that we envision

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection