OCCOM-efficient computation of observability-based code coverage metrics for functional verification

Being a good software testing engineer, one should have the responsibility towards environment sustainability. By using green principles and regulations, we can perform Green Software Testing. In this paper, we present a new approach to enhance Branch Coverage and Modified Condition/Decision Coverage uses concolic testing. We have proposed a novel transformation technique to improve these code coverage metrics. We have named this new transformation method Double Refined Code Transformer (DRCT). Then, using JoulMeter, we compute the power consumption and energy consumption in this testing process. We have developed a tool named Green-DRCT to measure energy consumption while performing the testing process.

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Comparing code coverage metrics for analog behavioral models

2017 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD) ◽

10.1109/smacd.2017.7981563 ◽

2017 ◽

Cited By ~ 2

Author(s):

Andreas Furtig ◽

Moritz Paschke ◽

Lars Hedrich

Keyword(s):

Behavioral Models ◽

Code Coverage ◽

Coverage Metrics

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Survey on Machine Learning Algorithms Enhancing the Functional Verification Process

Electronics ◽

10.3390/electronics10212688 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2688

Author(s):

Khaled A. Ismail ◽

Mohamed A. Abd El Ghany

Keyword(s):

Machine Learning ◽

Test Generation ◽

Machine Learning Algorithms ◽

Future Research ◽

Functional Verification ◽

Functional Requirements ◽

Comprehensive Overview ◽

Bug Detection ◽

Coverage Metrics ◽

Verification Process

The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred.

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