145 research outputs found

    Codeklonerkennung mit Dominatorinformationen

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    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

    Automated Software Transplantation

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    Automated program repair has excited researchers for more than a decade, yet it has yet to find full scale deployment in industry. We report our experience with SAPFIX: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code. We have used SAPFIX at Facebook to repair 6 production systems, each consisting of tens of millions of lines of code, and which are collectively used by hundreds of millions of people worldwide. In its first three months of operation, SAPFIX produced 55 repair candidates for 57 crashes reported to SAPFIX, of which 27 have been deem as correct by developers and 14 have been landed into production automatically by SAPFIX. SAPFIX has thus demonstrated the potential of the search-based repair research agenda by deploying, to hundreds of millions of users worldwide, software systems that have been automatically tested and repaired. Automated software transplantation (autotransplantation) is a form of automated software engineering, where we use search based software engineering to be able to automatically move a functionality of interest from a ‘donor‘ program that implements it into a ‘host‘ program that lacks it. Autotransplantation is a kind of automated program repair where we repair the ‘host‘ program by augmenting it with the missing functionality. Automated software transplantation would open many exciting avenues for software development: suppose we could autotransplant code from one system into another, entirely unrelated, system, potentially written in a different programming language. Being able to do so might greatly enhance the software engineering practice, while reducing the costs. Automated software transplantation manifests in two different flavors: monolingual, when the languages of the host and donor programs is the same, or multilingual when the languages differ. This thesis introduces a theory of automated software transplantation, and two algorithms implemented in two tools that achieve this: µSCALPEL for monolingual software transplantation and τSCALPEL for multilingual software transplantation. Leveraging lightweight annotation, program analysis identifies an organ (interesting behavior to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. We report encouraging results: in 14 of 17 monolingual transplantation experiments involving 6 donors and 4 hosts, popular real-world systems, we successfully autotransplanted 6 new functionalities; and in 10 out of 10 multlingual transplantation experiments involving 10 donors and 10 hosts, popular real-world systems written in 4 different programming languages, we successfully autotransplanted 10 new functionalities. That is, we have passed all the test suites that validates the new functionalities behaviour and the fact that the initial program behaviour is preserved. Additionally, we have manually checked the behaviour exercised by the organ. Autotransplantation is also very useful: in just 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player, a task that is currently done manually by the developers of VLC, since 12 years ago. We autotransplanted call graph generation and indentation for C programs into Kate, (a popular KDE based test editor used as an IDE by a lot of C developers) two features currently missing from Kate, but requested by the users of Kate. Autotransplantation is also efficient: the total runtime across 15 monolingual transplants is 5 hours and a half; the total runtime across 10 multilingual transplants is 33 hours

    Fundamental Approaches to Software Engineering

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    This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

    Computer Aided Verification

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    This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

    17th SC@RUG 2020 proceedings 2019-2020

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    17th SC@RUG 2020 proceedings 2019-2020

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    17th SC@RUG 2020 proceedings 2019-2020

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    17th SC@RUG 2020 proceedings 2019-2020

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    17th SC@RUG 2020 proceedings 2019-2020

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