Coverage directed test generation for functional verification using bayesian networks

Fine, Shai; Ziv, Avi

doi:10.1145/775832.775907

Cited by 152 publications

(29 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…2 and is called a "coverage-directed test generation". It has been introduced in [7] and later extended with use of evolutionary algorithms [12]. The main principle of this concept is to observe the impact of the stimuli on coverage.…”

Section: Improvements Of the Regression Testing Efficiencymentioning

confidence: 99%

Metric-Driven Verification Methodology with Regression Management

Cieplucha

2019

J Electron Test

View full text Add to dashboard Cite

This paper discusses the regression testing in digital integrated circuit verification. The aim of the metric-driven verification (MDV) and its role in modern verification methodology is presented. According to recognized limitations, it is proposed to include the regression testing strategy in verification environment itself. The data collected from the verification metrics can be used to adjust the testing procedures during the simulation. This approach allows for a dynamic management of the regression testing structure and may result in a significant reduction of the simulation time. The presented solution introduces a concept of the test segments. They may be started at arbitrary simulation point related to the DUT internal state (checkpoint). Usage of such segments, in the controlled regression testing strategy, may prevent from repeating the stimuli which is not contributing to the pre-defined verification metrics.

show abstract

Section: Improvements Of the Regression Testing Efficiencymentioning

confidence: 99%

Metric-Driven Verification Methodology with Regression Management

Cieplucha

2019

J Electron Test

View full text Add to dashboard Cite

show abstract

“…Besides mutation testing, code coverage is another widely used methodology for measuring test suite effectiveness in both academia and industry [73], [98], [99], [100], [101]. A huge body of research has been dedicated to study the relationship between test coverage and test effectiveness.…”

Section: Coverage-based Testingmentioning

confidence: 99%

Predictive mutation testing

Zhang

Wang

Zhang³

et al. 2016

Proceedings of the 25th International Symposium on Software Testing and Analysis

View full text Add to dashboard Cite

Test suites play a key role in ensuring software quality. A good test suite may detect more faults than a poor-quality one. Mutation testing is a powerful methodology for evaluating the fault-detection ability of test suites. In mutation testing, a large number of mutants may be generated and need to be executed against the test suite under evaluation to check how many mutants the test suite is able to detect, as well as the kind of mutants that the current test suite fails to detect. Consequently, although highly effective, mutation testing is widely recognized to be also computationally expensive, inhibiting wider uptake. To alleviate this efficiency concern, we propose Predictive Mutation Testing (PMT): the first approach to predicting mutation testing results without executing mutants. In particular, PMT constructs a classification model, based on a series of features related to mutants and tests, and uses the model to predict whether a mutant would be killed or remain alive without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while incurring only a small accuracy loss. It achieves above 0.80 AUC values for the majority of projects, indicating a good tradeoff between the efficiency and effectiveness of predictive mutation testing. Also, PMT is shown to perform well on different tools and tests, be robust in the presence of imbalanced data, and have high predictability (over 60% confidence) when predicting the execution results of the majority of mutants.

show abstract

“…Various code coverage and toggle coverage are the primary metrics. Recent research extends to more complex functional coverage [50] and assertion-based coverage.…”

Section: Wolfgang Müller and Tao Xiementioning

confidence: 99%

Methods of Improving the Dependability of Self-optimizing Systems

Dorociak

Gausemeier

2014

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Abstract. Various methods have been developed in the Collaborative ResearchCenter 614 which can be used to improve the dependability of self-optimizing systems. These methods are presented in this chapter. They are sorted into two categories with regard to the development process of self-optimizing systems. On one hand, there are methods which can be applied during the Conceptual Design Phase. On the other hand, there are methods that are applicable during Design and Development.There are domain-spanning methods as well as methods that have been specifically developed for particular domains, e.g., software engineering or control engineering. The methods address different attributes of dependability, such as reliability, availability or safety.Each section is prefaced with a short overview of the classification of the described method regarding the corresponding domain(s), as well as its dependability attributes, to provide the reader with a brief outline of the methods' areas of application. Information about independently applicable methods or existing relationships and interactions with other methods or third-party literature is also provided.The development process for self-optimizing mechatronic systems which was introduced in Chap. 2 consists of two main phases: Conceptual Design and Design and Development. The main result of the Conceptual Design is the Principle Solution, which includes all information required for the concrete development during the second phase. Conceptual Design PhaseEven as early as during the specification of the Principle Solution, the dependability of self-optimizing mechatronic systems can be evaluated and improved by employing appropriate methods. The result is an improved Principle Solution, as such methods take the whole system into account before splitting it up into domain-specific tasks for the following development phase.

show abstract

Coverage directed test generation for functional verification using bayesian networks

Cited by 152 publications

References 14 publications

Metric-Driven Verification Methodology with Regression Management

Metric-Driven Verification Methodology with Regression Management

Predictive mutation testing

Methods of Improving the Dependability of Self-optimizing Systems

Contact Info

Product

Resources

About