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An Initial Investigation of Neural Decompilation for WebAssembly ; En Första Undersökning av Neural Dekompilering för WebAssembly
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Benali, Adam. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2022
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Modelling, Reverse Engineering, and Learning Software Variability
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In: https://hal.inria.fr/tel-03521806 ; Software Engineering [cs.SE]. Université de Rennes 1, 2021 (2021)
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Data set of the article: Language Bias in the Google Scholar Ranking Algorithm ...
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Data set of the article: Language Bias in the Google Scholar Ranking Algorithm ...
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Specification-Based Protocol Obfuscation
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In: DSN 2018 - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks ; https://hal.inria.fr/hal-01848573 ; DSN 2018 - 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, Jun 2018, Luxembourg City, Luxembourg. pp.1-12, ⟨10.1109/DSN.2018.00056⟩ (2018)
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A Stochastic Petri Net Reverse Engineering Methodology for Deep Understanding of Technical Documents
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In: Browse all Theses and Dissertations (2018)
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Abstract:
Systems Reverse Engineering has gained great attention over time and is associated with numerous different research areas. The importance of this research derives from several technological necessities. Security analysis and learning purposes are two of them and can greatly benefit from reverse engineering. More specifically, reverse engineering of technical documents for deeper automatic understanding is a research area where reverse engineering can contribute a lot. In this PhD dissertation we develop a novel reverse engineering methodology for deep understanding of architectural description of digital hardware systems that appear in technical documents. Initially, we offer a survey on reverse engineering of electronic or digital systems. We also provide a classification of the research methods within this field, and a maturity metric is presented to highlight weaknesses and strengths of existing methodologies and systems that are currently available. A technical document (TD) is typically composed by several modalities, like natural language (NL) text, system's diagrams, tables, math formulas, graphics, pictures, etc. Thus, for automatic deep understanding of technical documents, a synergistic collaboration among these modalities is necessary. Here we will deal with the synergistic collaboration between NL-text and system's diagrams for a better and deeper understanding of a TD. In particular, a technical document is decomposed into two modalities NL-text and figures of system's diagrams. Then, the NL-text is processed with a Natural Language text Understanding (NLU) method and text sentences are categorized into five categories, by utilizing a Convolutional Neural Network to classify them accordingly. While, a Diagram-Image-Modeling (DIM) method processes the figures by extracting the system's diagrams. More specifically, NLU processes the text from the document and determines the associations among the nouns and their interactions, by creating their stochastic Petri-net (SPN) graph model. DIM performs processing/analysis of figures to transform the diagram into a graph model that holds all relevant information appearing in the diagram. Then, we combine (associate) these models in a synergistic way and create a synergistic SPN graph. From this SPN graph we obtain the functional specifications that form the behavior of the system in a form of pseudocode. In parallel we extract a flowchart to enhance the understanding that the reader could have about the pseudocode and the hardware system as a unity.
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Keyword:
Computer Engineering; Computer Sciences; Department of Computer Science and Engineering; Engineering; Physical Sciences and Mathematics; reverse engineering; stochastic Petri-net; systems reverse engineering; technical document
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URL: https://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=3087&context=etd_all https://corescholar.libraries.wright.edu/etd_all/1946
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Breathing Ontological Knowledge Into Feature Model Synthesis: An Empirical Study
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In: ISSN: 1382-3256 ; EISSN: 1573-7616 ; Empirical Software Engineering ; https://hal.inria.fr/hal-01096969 ; Empirical Software Engineering, Springer Verlag, 2015, pp.51. ⟨10.1007/s10664-014-9357-1⟩ (2015)
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Modeling of dynamic systems with Petri nets and fuzzy logic ...
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Mediating databases and the semantic web: a methodology for building domain ontologies from databases and existing ontologies
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Exampledriven reconstruction of software models
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In: http://www.iam.unibe.ch/~scg/Archive/Papers/Nier07aExampleDrivenMR.pdf (2007)
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Exampledriven reconstruction of software models
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In: http://www.inf.unisi.ch/faculty/lanza/Downloads/Nier07a.pdf (2007)
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Towards a standard schema for C/C++
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In: http://www.cs.toronto.edu/~simsuz/papers/ferencr_schema.pdf (2001)
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Semi-automatic Grammar Recovery
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In: http://adam.wins.uva.nl/~x/ge/ge.ps (2001)
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Generating Robust Parsers using Island Grammars
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In: http://www.cwi.nl/~leon/papers/wcre2001/wcre2001.ps.gz (2001)
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Generating Robust Parsers using Island Grammars
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In: http://www.cwi.nl/~leon/papers/wcre2001/wcre2001.pdf (2001)
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Automated Program Recognition by Graph Parsing
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In: DTIC AND NTIS (1992)
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Improving fact extraction of framework- based software system
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In: http://seal.ifi.unizh.ch/fileadmin/User_Filemount/Publications/knodel-wcre03.pdf
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uni-stuttgart.de
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In: http://www.inf.u-szeged.hu/~ferenc/research/ferencr_schema.pdf
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ABSTRACT Towards Supporting On-Demand Virtual Remodularization Using Program Graphs
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In: http://www.cis.udel.edu/~shepherd/shepherdAosd2006.pdf
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Reusable Parser Generation from Free and Open Source Compilers
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In: http://ww1.ucmss.com/books/LFS/CSREA2006/SER7242.pdf
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