Functional verification methodology for microprocessors using the Genesys test-program generator. Application to the x86 microprocessors family

Fournier, Laurent Sébastien; Arbetman, Y.; Levinger, M.

doi:10.1109/date.1999.761162

Cited by 23 publications

(9 citation statements)

References 6 publications

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“…IBM Research [3,5,6] has been one of the main contributors in the field of test program generation for microprocessors during the last decades. The first test generation tools were developed in the middle of 1980s.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…The paper aims to contribute to the research in the field as the lack of information on competitors' solutions makes it difficult to apply their ideas. The paper summarizes the ideas from different sources [4, 5,6], proposes some important improvements and expresses our vision for organization of a knowledgebase for test generation.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…It implies separation of knowledge of the verified system's configuration from knowledge of test generation techniques. The former is referred to as a model and the latter is often called testing knowledge [5,6]. The model can be either created manually or built automatically on the basis of formal specifications.…”

Section: Introductionmentioning

confidence: 99%

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A Generic Knowledgebase for Test Generation

Kotsynyak¹,

Tatarnikov²

2014

Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering

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Abstract-Nowadays a lot of various test generation tools are developed and applied to create tests for both software applications and hardware designs. Taking into account the size and complexity of modern projects, there is an urgent need for "smart" tools that would help maximize test coverage and keep the required effort and time to a minimum. Despite the fact that each project is unique in some sense, there is a set of common generation techniques that are applied in a wide range of projects (random tests, combinatorial tests, tests for corner cases, etc). In addition, projects belonging to specific domains tend to share similar test cases or use similar heuristics to generate them. A natural way to improve the quality of testing is to make the most of the experience gained working on different projects or performing testing at different stages of the same project. To achieve this goal, a knowledgebase holding information relevant to test generation would be of a great help. This would facilitate reuse of test cases and generation algorithms and would allow sharing knowledge of "interesting" situations that can occur in a system under test. The paper proposes a concept of a knowledgebase for test generation that can be used in a wide range of test generation tools. At ISPRAS, it is applied in test program generation tools that create test programs for microprocessors. The knowledgebase is designed to store information on widely used test generation techniques and test situations that can occur in a microprocessor design under verification.

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Section: Related Work and Motivationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

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A Generic Knowledgebase for Test Generation

Kotsynyak¹,

Tatarnikov²

2014

Proceedings of the Spring/Summer Young Researchers' Colloquium on Software Engineering

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“…These test generators were primarily developed to minimize the effort of architecture-based test generation and to allow the usual architectural changes and upgrades to be easily modeled within the tool [5][2]. Their Model-Based architecture provides the end-user the ability to incrementally enrich the output of the generator (i.e.…”

Section: Model-based Test Generatorsmentioning

confidence: 99%

“…However, one should be aware of the limitations of random testing. Indeed, pure random testing is inefficient since the space domain is enormous and the probability of interesting cases is infinitely small [5]. As such, random test generators have evolved into directed-random and targeted-random generation.…”

Section: Figure2 Coverage Recovery and Saturation After Resetmentioning

confidence: 99%

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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Coverage Driven Test Generation and Consistency Algorithm

Paret

Mohamed

2014

Declarative Programming and Knowledge Management

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Functional verification methodology for microprocessors using the Genesys test-program generator. Application to the x86 microprocessors family

Cited by 23 publications

References 6 publications

A Generic Knowledgebase for Test Generation

A Generic Knowledgebase for Test Generation

Coverage-directed management and optimization of random functional verification

Coverage Driven Test Generation and Consistency Algorithm

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