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16 Reverse Engineering – Recent Advances and Applications 14 Will-be-set-by-IN-TECH �� Fig. 3. Architecture of the RTSSim tool. �� Examples of simulation tools are ARTISST (www.irisa.fr/aces/software/artisst/) (Decotigny & Puaut, 2002), DRTSS (Storch & Liu, 1996), RTSSim (Kraft, 2009), and VirtualTime (www.rapitasystems.com/virtualtime). Since these tools have similar capabilites we only describe RTSSim in more detail. RTSSim was developed for the purpose of simulation-based analysis of run-time properties related to timing, performance and resource usage, targeting complex embedded systems where such properties are otherwise hard to predict. RTSSim has been designed to provide a generic simulation environment which provides functionality similar to most real-time operating systems (cf. Figure 3). It offers support for tasks, mailboxes and semaphores. Tasks have attributes such as priority, periodicity, activation time and jitter, and are scheduled using preemptive fixed-priority scheduling. Task-switches can only occur within RTSSim API functions (e.g., during a “hold CPU”); other model code always executes in an atomic manner. The simulation can exhibit “stochastic” behavior via random variations in task release time specified by the jitter attribute, in the increment of the simulation clock, etc. To obtain timing properties and traces, the simulation has to be driven by suitable input. A typical goal is to determine the highest observed response time for a certain task. Thus, the result of the simulation greatly depends on the chosen sets of input. Generally, a random search (traditional Monte Carlo simulation) is not suitable for worst-case timing analysis, since a random subset of the possible scenarios is a poor predictor for the worst-case execution time. Simulation optimization allows for efficient identification of extreme scenarios with respect to a specified measurable run-time property of the system. MABERA and HCRR are two heuristic search methods for RTSSim. MABERA (Kraft et al., 2008) is a genetic algorithm that treats RTSSim as a black-box function, which, given a set of simulation parameters, outputs the highest response-time found during the specified simulation. The genetic algorithm determines how the simulation parameters are changed for the next search iteration. HCRR (Bohlin et al., 2009), in contrast, uses a hill climbing algorithm. It is based on the idea of starting at a random point and then repeatedly taking small steps pointing “upwards”, i.e., to nearby input combinations giving higher response times. Random restarts are used to avoid getting stuck in local maxima. In a study that involved a subset of an industrial complex embedded system, HCRR performed substantially better than both Monte Carlo simulation and the MABERA (Bohlin et al., 2009).
Software Reverse Engineering in the Domain of Complex Embedded Systems Software Reverse Engineering in the Domain of Complex Embedded Systems 15 Fig. 4. Conceptual view of model validation with tracing data. It is desirable to have a model that is substantially smaller than the real system. A smaller model can be more effectively simulated and reasoned about. It is also easier to evolve. Since the simulator can run on high-performance hardware and the model contains only the characteristics that are relevant for the properties in focus, a simulation run can be much faster than execution of the real system. Coupled with simulator optimization, significantly more (diverse) scenarios can be explored. A simulation model also allows to explore scenarios which are difficult to generate with the real system, and allows impact analyses of hypothetical system changes. Reverse engineering is used to construct such simulation models (semi-)automatically. The MASS tool (Andersson et al., 2006) supports the semi-automatic extraction of simulation models from C code. Starting from the entry function of a task, the tool uses dependency analysis to guide the inclusion of relevant code for that task. For so-called model-relevant functions the tool generates a code skeleton by removing irrelevant statements. This skeleton is then interactively refined by the user. Program slicing is another approach to synthesize models. The Model eXtraction Tool for C (MTXC) (Kraft, 2010, chapter 6) takes as input a set of model focus functions of the real system and automatically produces a model via slicing. However, the tool is not able to produce an executable slice that can be directly used as simulation input. In a smaller case study involving a subset of an embedded system, a reduction of code from 3994 to 1967 (49%) lines was achieved. Huselius & Andersson (2005) describe a dynamic approach to obtain a simulation model based on tracing data containing interprocess communications from the real system. The raw tracing data is used to synthesize a probabilistic state-machine model in the ART-ML modeling language, which can be then run with a simulator. For each model that abstracts from the real system, there is the concern whether the model’s behavior is a reasonable approximation of the real behavior (Huselius et al., 2006). This concern is addressed by model validation, which can be defined as the “substantiation that a computerized model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model” (Schlesinger et al., 1979). Software simulation models can be validated by comparing trace data of the real system versus the model (cf. Figure 4). There are many possible approaches, including statistical and subjective validation techniques (Balci, 1990). Kraft describes a five-step validation process that combines both subjective and statistical comparisons of tracing data (Kraft, 2010, chapter 8). 4.4 Comparison of approaches In the following we summarize and compare the different approaches to timing analysis with respect to three criteria: soundness, scalability and applicability to industrial complex 17
Page 1 and 2: REVERSE ENGINEERING - RECENT ADVANC
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108 Table 1. Number of smoothers an
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1. Introduction 60 Surface Reconstr
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Surface Reconstruction from Unorgan
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1. Introduction A Systematic Approa
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A Systematic Approach for Geometric
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A Review on Shape Engineering and D
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Integrating Reverse Engineering and
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Part 3 Reverse Engineering in Medic
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238 5. Summary and discussions Reve
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268 Fig. 6. A closed polygon mesh r
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272 3. Results Reverse Engineering
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