Constraint satisfaction for test program generation

Lewin, Daniel R.; Fournier, Laurent Sébastien; Levinger, M.; Roytman, E.; Shurek, Gil

doi:10.1109/pccc.1995.472513

Cited by 15 publications

(11 citation statements)

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“…Despite theoretical limitations of automatic test case generation 2 , dealing with these hard combinatorial problems for real-world programs is challenging. Constraint-based testing was introduced to tackle these problems for various applications, including automatic white-box test inputs generation for imperative programs [3][4][5][6][7][8][9][10][11][12] and object-oriented programs [13][14][15][16][17][18], test pattern generation in hardware design [19][20][21], model-based test cases generation [22][23][24][25][26][27], and counter-examples generation [28][29][30][31].…”

Section: Constraint-based Testingmentioning

confidence: 99%

Constraint-Based Testing

Gotlieb

2015

Advances in Computers

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Section: Constraint-based Testingmentioning

confidence: 99%

Constraint-Based Testing

Gotlieb

2015

Advances in Computers

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“…This problem originated from the field of automatic generation of test patterns for hardware verification, see e.g. [6,7]. In [9] the authors also studied the problem in which we are looking for a shortest DNF F representing f (given by an interval [a,b]) such that for any vector x, for which f (x) = 1 there is exactly one term t in F for which t(x) = 1.…”

Section: Introductionmentioning

confidence: 99%

Recognition of interval Boolean functions

Čepek

Kronus

Kučera

2008

Ann Math Artif Intell

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Interval functions constitute a special class of Boolean functions for which it is very easy and fast to determine their functional value on a specified input vector. The value of an n-variable interval function specified by interval [a, b ] (where a and b are n-bit binary numbers) is true if and only if the input vector viewed as an n-bit number belongs to the interval [a, b ]. In this paper we study the problem of deciding whether a given disjunctive normal form represents an interval function and if so then we also want to output the corresponding interval. For general Boolean functions this problem is co-NP-hard. In our article we present a polynomial time algorithm which works for monotone functions. We shall also show that given a Boolean function f belonging to some class C which is closed under partial assignment and for which we are able to solve the satisfiability problem in polynomial time, we can also decide whether f is an interval function in polynomial time. We show how to recognize a "renamable" variant of interval functions, i.e., their variable complementation closure. Another studied problem is the problem of finding an interval extension of partially defined Boolean functions. We also study some other properties of interval functions.

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“…A model-based system operates on a model of the structure and behavior of a device or the function that a system is designed to simulate [2]. Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do).…”

Section: Model-based Systemsmentioning

confidence: 99%

“…Model-based test generators such as GenesysPro require continuous calibration of their autonomous components (Generator, Knowledge-base and Reference model) to ensure high quality tests are generated (Figure 1). An ideal model-based test generator is one where the generator is 100% independent of the application domain (i.e.. it has no explicit knowledge of domain structure or behavior) and solely relies on the generation knowledge-base to solve the problem at hand [6][3], perhaps based on a set of constraint satisfaction algorithms [2]. Therefore, each installation of GenesysPro is in fact a custom environment and therefore methods have to be devised for fine-tuning of the components of this model-based reasoning (MBR) system.…”

Section: Model-based Test Generatorsmentioning

confidence: 99%

“…In the advent of the ever-increasing complexity of designs, decreasing design cycles, and cost constrained projects resulting in increased burden on verification engineers, processor design teams are becoming increasingly dependent on automatic test generators. A model-based system operates on a model of the structure and behavior of a device or the function that a system is designed to simulate [2]. Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do).…”

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Coverage-directed management and optimization of random functional verification

Hekmatpour

Coulter

International Test Conference, 2003. Proceedings. ITC 2003.

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This paper describes a functional verification methodology based on a system developed at the IBM Microelectronics Embedded PowerPC Design Center, in order to improve the coverage and convergence of random test generators in general and model-based random test generators in particular. It outlines specific tasks and methods devised for qualifying the test generators at various stages of the functional verification process to ensure the integrity of generated tests. It describes methods for calibrating the test generation process to improve functional coverage. In addition, it outlines a strategy for improved management and control of the test generation for faster convergence across corner cases, complex scenarios, and deep interdependencies. The described methodology and its associated verification platform are deployed at the IBM Embedded PowerPC Design Center in Research Triangle Park, North Carolina and has been used in the verification of 4XX and 4XXFPU family of PowerPC Processors. IntroductionTest generators have become an important part of functional verification. In the advent of the ever-increasing complexity of designs, decreasing design cycles, and cost constrained projects resulting in increased burden on verification engineers, processor design teams are becoming increasingly dependent on automatic test generators. A model-based system operates on a model of the structure and behavior of a device or the function that a system is designed to simulate [2]. Observed behavior (what the device is actually doing) is compared with predicted behavior (what the device should do). The differences between observed behavior and predicted behavior are identified as discrepancies, indicating potential defects. The inference component of such a model-based system (e.g. its model-based reasoning engine) is then initiated to diagnose the nature and location of any defects.A model-based system is usually comprised of several independent components (i.e. models, methods, inference) [3]. Any result generated is based on and influenced by all relevant models and methods. Changes in the inference will impact the quality of the output results for the same set of models and changes in any of the models will impact the result of such a system even if the inference and generation methods remain the same. Ensuring the integrity and quality of solutions generated is an important ongoing activity in development and maintenance of such systems. Providing feedback and guidance to users of such a system on proper utilization and adjustments required to existing methods and procedures (i.e. what adjustments users have to make to the models or methods they have already developed) is another important ongoing activity in such an environment [4].

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Constraint satisfaction for test program generation

Cited by 15 publications

References 1 publication

Constraint-Based Testing

Constraint-Based Testing

Recognition of interval Boolean functions

Coverage-directed management and optimization of random functional verification

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