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<p> In this day and age, many companies struggle with the maintenance of legacy scientific software systems written in outdated programming languages. These languages use low-level control structures, algorithmic operations and cumbersome syntax that make the true meaning of an algorithm difficult to understand. To make matters worse, the process of reverse engineering the algorithm to specification often involves a considerable amount of manual work which is error-prone and time-consuming.</p> <p> This thesis explores a completely automated method of reverse engineering. We apply this method to FORTRAN77 linear algebra software. This software is transformed to an extension of FORTRAN77, which we call Fortran-M. This language allows for high-level mathematical constructs such as sums, products and vector and matrix operations. To serve as a proof-of-concept for this method, we have developed a tool which uses a combination of pattern matching on the source code's abstract syntax tree to recognise low-level control structures, and symbolic analysis to determine the meaning of loops. Once a pattern has been recognised, or a loop's invariant found, we apply transformations to the syntax tree, thus creating a Fortran-M equivalent.</p> / Thesis / Master of Applied Science (MASc)

Alstom Power Sweden AB in Norrköping isexpanding their business in the presentsituation and has implemented aprocedure for reverse engineering as apart of this work. Reverse engineeringat Alstom is an extensive procedurethat is calculated to be performedunder very time demanding conditions. This thesis is a continued exertion ofthe implementation process in order toidentify and describe the mostimportant process steps in purpose to beable to manage and improve theprocess.This thesis was written with twoprimary objectives. The first was todescribe and map out the procedure torecreate technical data of a turbineblade, based on a validation of thereverse engineering process that hasbeen performed at Alstom in Norrköping.The second purpose was to describe thekey factors to achieve successful usageof reverse engineering that have beennoticed in the literature study. The work is based on a literature studyon the subject reverse engineeringwhich produced the theoreticalknowledge of the method to reproducetechnical data to a product. Theliterature study has also led to adescription of reverse engineering froma legal perspective to clarify how farit is allowed to apply the methodaccording to the patent laws. With theliterature study as support and furtherhelp and guidance from individualswithin the reverse engineering unit atAlstom, a complete picture andunderstanding of the process has beenproduced and documented.

In the post-"omic" era the analysis of high-throughput data is regarded as one of the major challenges faced by researchers. One focus of this data analysis is uncovering biological network topologies and dynamics. It is believed that this kind of research will allow the development of new mathematical models of biological systems as well as aid in the improvement of already existing ones. The work that is presented in this dissertation addresses the problem of the analysis of highly complex data sets with the aim of developing a methodology that will enable the reconstruction of a biological network from time series data through an iterative process. The first part of this dissertation relates to the analysis of existing methodologies that aim at inferring network structures from experimental data. This spans the use of statistical tools such as correlations analysis (presented in Chapter 2) to more complex mathematical frameworks (presented in Chapter 3). A novel methodology that focuses on the inference of biological networks from time series data by least squares fitting will then be introduced. Using a set of carefully designed inference rules one can gain important information about the system which can aid in the inference process. The application of the method to a data set from the response of the yeast Saccharomyces cerevisiae to cumene hydroperoxide is explored in Chapter 5. The results show that this method can be used to generate a coarse-level mathematical model of the biological system at hand. Possible developments of this method are discussed in Chapter 6. / Ph. D.

Reverse engineering - defined as extracting information about a product from the product itself - is a design tactic commonly used in industry from competitive benchmarking to product imitation. While reverse engineering is a legitimate practice - as long as the product was legally obtained - innovative products are often reverse engineered at the expense of the pioneering company. However, by designing products with built-in barriers to reverse engineering, competitors are no longer able to effectively extract critical information from the product of interest. Enabling the quantification of barriers to reverse engineering, this dissertation presents a set of metrics and parameters that can be used to calculate the barrier to reverse engineer any product as well as the time required to do so. To the original designer, these numerical representations of the barrier and time can be used to strategically identify and improve product characteristics so as to increase the difficulty and time to reverse engineer them. On the other hand, these quantitative measures enable competitors who reverse engineer original designs to focus their efforts on products that will result in the greatest return on investment. In addition to metrics that estimate the reverse engineering barrier and time, this dissertation also presents a methodology to strategically plan for, select, design, and implement reverse engineering barriers. The methodology presented herein considers barrier development cost, barrier effectiveness in various product components, impact on performance, and return on investment. This process includes sensitivity analysis, modeling of the return on investment, and exploration of multiobjective design spaces. The effectiveness of the presented methodology is demonstrated by making a solar-powered unmanned aerial vehicle difficult to reverse engineer. In the example, the propeller is selected to be the critical component where a series of voids are introduced to decrease the propeller weight and increase the flutter speed (a desirable attribute in propellers). Our tenet is that the use of such a framework contributes greatly to the sustainability of technological, economical, and security advantages enjoyed by those who developed the technology. Designers benefit because (i) products do not readily disclose trade secrets, (ii) competitive advantages can be maintained by impeding competitors from reverse engineering and imitating innovative products, and (iii) the return on investment can be increased.

Aus der Einleitung: "Im Modell und Formenbau des Maschinenbaus beschreibt Reverse Engineering den Prozess der 3D-Erfassung eines Objektes, die Aufbereitung der Digitalisierungsdaten zu CAD-Modellen und die weitere Nutzung dieser Daten in einer CAD/CAM-Umgebung. Ziel dieser Arbeiten ist es dann weiterführend, physische Objekte durch CNC-Fräsen oder mittels Generativer Fertigungsverfahren herzustellen. Die Maßkontrolle der gefertigten Produkte gegenüber dem CAD ist ebenfalls eine Aufgabestellung des Reverse Engineering (Schöne 2009, Wang 2011)."

Genregulation bezeichnet die geregelte Steuerung der Genexpression durch das Zusammenspiel einer Vielzahl von Transkriptionsfaktoren die in ihrer Gesamtheit hoch komplexe und zell-spezifische genregulatorische Netzwerke bilden. Im Rahmen meiner Arbeit beschäftigte ich mich mit zwei Ansätzen der computergestützten Analyse solcher Netzwerke, Modellierung und Reverse Engineering. Der erste Teil meiner Arbeit beschreibt die Entwicklung der Web-Anwendung GEne Network GEnerator (GeNGe). Hierbei handelt es sich um ein System für die automatische Erzeugung von genregulatorischen Netzwerken. Hierfür entwickelte und implementierte ich einen neuartigen Algorithmus für die Generierung von Netzwerkstrukturen die wichtige Eigenschaften biologischer Netzwerke zeigen. Für die dynamische Beschreibung der Transkription modifizierte ich eine nicht-lineare Kinetik. Diese neue Formulierung der Kinetik eignet sich besonders für die Erstellung von komplexen genregulatorischen Modellen am Computer. Desweiteren unterstützt GeNGe die Durchführung verschiedener in silico Experimente, um theoretische Aussagen über den Einfluss von Störungen des Systems treffen zu können. Der zweite Teil meiner Arbeit beschreibt die Entwicklung von GNRevealer. Es handelt sich hierbei um eine Methode zur Rekonstruktion von genregulatorischen Netzwerken auf Basis zeitdiskreter Messungen der Genexpression. Diese Methode verwendet ein neuronales Netz zusammen mit einem passenden Lernalgorithmus (backpropagation through time). Modifizierungen, welche notwendig für die Anwendung im Reverse Engineering Bereich sind, wurden von mir entwickelt, wie z.B. die Etablierung eines vollständigen Lernprozesses, die Diskretisierung der Ergebnisse und anschließende Validierungen. Im letzten Teil dieser Arbeit beschreibe ich eine Studie, in der sechs verschiedene Reverse Engineering Anwendungen von mir miteinander verglichen wurden. Diese Untersuchung hebt GNRevealer als geeignetste Anwendung aller getesteten Methoden hervor. / Gene regulation is accomplished mainly by the interplay of multiple transcription factors. This gives rise to highly complex and cell-type specific, interwoven structures of regulatory interactions summarized in gene regulatory networks. In this thesis, I address two approaches of computational analysis of such networks, forward modeling and reverse engineering. The first part of this thesis is about the Web application GEne Network GEnerator (GeNGe) which I have developed as a framework for automatic generation of gene regulatory network models. I have developed a novel algorithm for the generation of network structures featuring important biological properties. In order to model the transcriptional kinetics, I have modified an existing non-linear kinetic. This new kinetic is particularly useful for the computational set-up of complex gene regulatory models. GeNGe supports also the generation of various in silico experiments for predicting effects of perturbations as theoretical counterparts of biological experiments. Moreover, GeNGe facilitates especially the collection of benchmark data for evaluating reverse engineering methods. The second part of my thesis is about the development of GNRevealer, a method for reverse engineering of gene regulatory networks from temporal data. This computational approach uses a neural network together with a sophisticated learning algorithm (backpropagation through time). Specialized features developed in the course of my thesis include essential steps in reverse engineering processes such as the establishment of a learning workflow, discretization, and subsequent validation. Additionally, I have conducted a large comparative study using six different reverse engineering applications based on different mathematical backgrounds. The results of the comparative study highlight GNRevealer as best performing method among those under study.

Aus der Einleitung: "Im Modell und Formenbau des Maschinenbaus beschreibt Reverse Engineering den Prozess der 3D-Erfassung eines Objektes, die Aufbereitung der Digitalisierungsdaten zu CAD-Modellen und die weitere Nutzung dieser Daten in einer CAD/CAM-Umgebung. Ziel dieser Arbeiten ist es dann weiterführend, physische Objekte durch CNC-Fräsen oder mittels Generativer Fertigungsverfahren herzustellen. Die Maßkontrolle der gefertigten Produkte gegenüber dem CAD ist ebenfalls eine Aufgabestellung des Reverse Engineering (Schöne 2009, Wang 2011)."