Modelica is a modern, strongly typed, declarative, equation-based, and object-oriented (EOO) language for modeling and simulation of complex cyber-physical systems. Major features are: ease of use, visual design of models with combination of lego-like predefined model building blocks, ability to define model libraries with reusable components, support for modeling and simulation of complex applications involving parts from several application domains, and many more useful facilities. This paper gives an overview of some aspects of the Modelica language and the OpenModelica environment - the most complete Modelica open-source tool for modeling, simulation, and development of Modelica applications. Special features are MetaModeling for efficient model transformations, the ModelicaML profile for UML-Modelica cyber-physical hardware-software modeling, as well as generation of parallel code for multi-core architectures.

Computer aided modeling and simulation of complex physical systems, using components from multiple application domains, such as electrical, mechanical, and hydraulic, have in recent years witnessed a significant growth of interest. In the last decade, equation-based object-oriented (EOO) modeling languages, (e.g. Modelica, gPROMS, and VHDL-AMS) based on acausal modeling using Differential Algebraic Equations (DAEs), have appeared. With such languages, it is possible to model physical systems at a high level of abstraction by using reusable components. A model in an EOO language needs to have the same number of equations as unknowns. A previously unsolved problem concerning this property is the efficient detection of over- or under-constrained models in the case of separately compiled models. This paper describes a novel technique to determine over- and under-constrained systems of equations in models, based on a concept called structural constraint delta. In many cases it is also possible to locate the source of the constraint-problem. Our approach makes use of static type checking and consists of a type inference algorithm. We have implemented it for a subset of the Modelica language, and successfully validated it on several examples.

Mathematical modeling and simulation of complex physical systems are emerging as key technologies in engineering. Modern approaches to physical system simulation allow users to specify simulation models with the help of equation-based languages. Due to the high-level declarative abstraction of these languages program errors are extremely hard to find. This paper presents an algorithmic semi-automated debugging framework for equation-based modeling languages. We show how program slicing and dicing performed at the intermediate code level combined with assertion checking techniques can automate, to a large extent, the error finding process and behavior verification for physical system simulation models.

Summary form only given. We propose a lossless algorithm of delta compression (a variant of predictive coding) that attempts to predict the next point from previous points using higher-order polynomial extrapolation. In contrast to traditional predictive coding our method takes into account varying (non-equidistant) domain (typically, time) steps. To save space and guarantee lossless compression, the actual and predicted values are converted to 64-bit integers. The residual (difference between actual and predicted values) is computed as difference of integers. The unnecessary bits of the residual are truncated, e.g., 1111110101 is replaced by 10101. The length of the bit sequence (5/sub 10/=(000101)/sub 2/) is prepended.

The complexity of mechanical and multi-domain simulation models is rapidly increasing. Therefore new methods and standards are needed for model design. A new language, Modelica, has been proposed by an international design committee as a standard, object oriented, equation based language suitable for description of the dynamics of systems containing mechanical, electrical, chemical and other types of components. However, it is complicated to describe the system models in textual form, whereas CAD systems are convenient tools for this purpose. We have designed an environment that supports the translation from CAD models to standard Modelica notation. This notation is then used for simulation and visualization. Assembly information is extracted from the CAD models, from which a Modelica model is generated. By solving equations expressed in Modelica, the system is simulated. A 3D visualization tool based on OpenGL visualizes expected and actual model behavior, as well as additional parameters. The environment has been applied for robot and flight simulation.

PARFORMAN (PARallel FORMal ANnotation language) is a high-level specification language for expressing intended behavior or known types of error conditions when debugging or testing parallel programs. Models of intended or faulty target program behavior can be succinctly specified in PARFORMAN. These models are then compared with the actual behavior in terms of execution traces of events, in order to localize possible bugs. PARFORMAN can also be used as a general language for expressing computations over target program execution histories. PARFORM AN is based on a precise model of target program behavior. This model, called H-space (History-space), is formally defined through a set of general axioms about three basic relations, which may or may not hold between two arbitrary events: they may be sequentially ordered (SEQ), they may be parallel (PAR), or one of them might be included in another composite event (IN). The general notion of composite event is exploited systematically, which makes possible more powerful and succinct specifications. The notion of event grammar is introduced to describe allowed event patterns over a certain application domain or language. Auxiliary composite events such as Snapshots are introduced to be able to define the notion “occurred at the same time” at suitable levels of abstraction. Finally, patterns and aggregate operations on events are introduced to make possible short and readable specifications. In addition to debugging and testing, PARFORMAN can also be used to specify profiles and performance measurements.

For a long time efficient use of parallel computers has been hindered by dependencies introduced in software through low-level implementation practice. In this paper we present a programming environment and language called Object-Math (Object oriented Mathematical language for scientific computing), which aims at eliminating this problem by allowing the user to represent mathematical equation-based models directly in the system. The system performs analysis of mathematical models to extract parallelism and automatically generates parallel code for numerical solution.

Abstract Lazy functional languages have non-strict semantics and are purely declarative, i.e. they support the notion of referential transparency and are devoid of side-effects. Traditional debugging techniques are, however, not suited for lazy functional languages, since computations generally do not take place in the order one might expect. Since algorithmic debugging allows the user to concentrate on the declarative aspects of program semantics, and will semi-automatically find functions containing bugs, we propose to use this technique for debugging lazy functional programs. Because of the non-strict semantics of lazy functional languages, arguments to functions are in general partially evaluated expressions. The user is, however, usually more concerned with the values that these expressions represent. We address this problem by providing the user with a strictified view of the execution trace whenever possible. In this paper, we present an algorithmic debugger for a lazy functional language based on strictification and some experience in using it. A number of problems with the current implementation of the debugger (e.g. too large trace size and too many questions asked) are also discussed and some techniques for overcoming these problems, at least partially, are suggested. The key techniques are immediate strictification and piecemeal tracing .

During the past ten years several variants of an analysis technique called program slicing have been developed. Program slicing has applications in maintenance tasks such as debugging, testing, program integration, program verification, etc. and can be characterized as a type of dependence analysis. A program slice can loosely be defined as the subset of a program needed to compute a certain variable value at a certain program position. A novel method for interprocedural dynamic slicing which is more precise than interprocedural static slicing methods and is useful for dependence analysis at the procedural abstraction level was given by M. Kamkar et al. (1992, 1993). It is demonstrated here how interprocedural dynamic slicing can be used to increase the reliability and precision of interprocedural data flow testing. The work on data flow testing reported by E. Duesterwald et al. (1992), which is a novel method for data flow testing through output influences, is generalized.>

In this paper we present the ObjectMath language, a hybrid lan- guage integrating object-oriented constructs with features for symbolic and numeric computation.Complex mathematical models may be implemented in a natural way using ObjectMath.Objects are used to structure models that might consist of a large number of equations while inheritance facilitates reuse of equations and formulae.The class specialization hierarchy is also used to generate spxialized efficient numeric code from general symbolic equations and expressions.A first prototype of the ObjectMath programming environment has bean implemented and successfully used for modeling and analyzing two different machine element analysis problems.

This paper presents a method for semi-automatic bug localization, generalized algorithmic debugging, which has been integrated with the category partition method for functional testing. In this way the efficiency of the algorithmic debugging method for bug localization can be improved by using test specifications and test results. The long-range goal of this work is a semi-automatic debugging and testing system which can be used during large-scale program development of nontrivial programs. The method is generally applicable to procedural languages and is not dependent on any ad hoc assumptions regarding the subject program. The original form of algorithmic debugging, introduced by Shapiro, was however limited to small Prolog programs without side-effects, but has later been generalized to concurrent logic programming languages. Another drawback of the original method is the large number of interactions with the user during bug localization. To our knowledge, this is the first method which uses category partition testing to improve the bug localization properties of algorithmic debugging. The method can avoid irrelevant questions to the programmer by categorizing input parameters and then match these against test cases in the test database. Additionally, we use program slicing, a data flow analysis technique, to

An algorithmic program debugger for imperative languages is presented, with Pascal as an example case. This debugger extends the power of existing debuggers by providing an interactive debugging facility where errors can be localized semiautomatically. The debugger is activated on demand when the user discovers a symptom of an error as the result of some computation. This symptom presumably denotes a difference between the intended program behavior and the actual behavior. The proposed approach consists of three phases: program transformation, tracing, and debugging. The first phase transforms the source program into an internal representation which is appropriate, according to the Shapiro model, for algorithmic debugging. This phase produces an intermediate program which is free from side effects and loops. The program tracing phase generates trace information which builds an execution tree for the erroneous program. The debugging phase performs bug localization through a number of user interactions. This phase consists of pure algorithmic program debugging and program slicing.>