ABSTRACT
The ever-present challenge in the domain of digital devices is how to test their behavior efficiently. We tackle the issue in two ways. We switch to an automated circuit design using Grammatical Evolution (GE). Additionally, we provide two diversity-based methodologies to improve testing efficiency. The first approach extracts a minimal number of test cases from subsets formed through clustering. Moreover, the way we perform clustering can easily be used for other domains as it is problem-agnostic. The other uses complete test set and introduces a novel fitness function hitPlex that incorporates a test case diversity measure to speed up the evolutionary process.
Experimental and statistical evaluations on six benchmark circuits establish that the automatically selected test cases result in good coverage and enable the system to evolve a highly accurate digital circuit. Evolutionary runs using hitPlex indicate promising improvements, with up to 16% improvement in convergence speed and up to 30% in success rate for complex circuits when compared to the system without the diversity extension.
- Prabhleen Bindra, Meghana Kshirsagar, Conor Ryan, Gauri Vaidya, Krishn Kumar Gupt, and Vivek Kshirsagar. 2021. Insights into the Advancements of Artificial Intelligence and Machine Learning, the Present State of Art, and Future Prospects: Seven Decades of Digital Revolution.Google Scholar
- Tsong Yueh Chen. 2010. Fundamentals of test case selection: Diversity, diversity, diversity. In The 2nd International Conference on Software Engineering and Data Mining. IEEE, 723--724.Google Scholar
- Pedro Contreras and Fionn Murtagh. 2015. Hierarchical clustering. In Handbook of cluster analysis. Chapman and Hall/CRC, 124--145.Google Scholar
- Robert Feldt, Simon Poulding, David Clark, and Shin Yoo. 2016. Test set diameter: Quantifying the diversity of sets of test cases. (2016), 223--233.Google Scholar
- Mohammed Ferdjallah. 2011. Introduction to digital systems: modeling, synthesis, and simulation using VHDL. John Wiley & Sons.Google Scholar
- Krishn Kumar Gupt, Meghana Kshirsagar, Joseph P Sullivan, and Conor Ryan. 2021. Automatic Test Case Generation for Prime Field Elliptic Curve Cryptographic Circuits. In 2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA). IEEE, 121--126.Google Scholar
- Krishn Kumar Gupt, Meghana Kshirsagar, Joseph P Sullivan, and Conor Ryan. 2021. Automatic test case generation for vulnerability analysis of galois field arithmetic circuits. In 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP). IEEE, 32--37.Google ScholarCross Ref
- J R Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA.Google ScholarDigital Library
- Meghana Kshirsagar, Rushikesh Jachak, Purva Chaudhari, and Conor Ryan. 2020. GEMO: Grammatical Evolution Memory Optimization System.. In IJCCI. 184--191.Google Scholar
- A Albert Raj and T Latha. 2008. VLSI design. PHI Learning Pvt. Ltd.Google Scholar
- Conor Ryan, John James Collins, and Michael O Neill. 1998. Grammatical evolution: Evolving programs for an arbitrary language. In European Conference on Genetic Programming. 83--96.Google ScholarCross Ref
- Conor Ryan, Meghana Kshirsagar, Krishn Kumar Gupt, Lukas Rosenbauer, and Joseph P Sullivan. 2021. Hierarchical Clustering Driven Test Case Selection in Digital Circuits.Google Scholar
- Conor Ryan, Michael O'Neill, and JJ Collins. 2018. Handbook of Grammatical Evolution. (2018).Google Scholar
- Michael Kwaku Tetteh, Douglas Mota Dias, and Conor Ryan. 2021. Evolution of Complex Combinational Logic Circuits Using Grammatical Evolution with SystemVerilog. In European Conference on Genetic Programming (Part of EvoStar). Springer, 146--161.Google Scholar
Index Terms
- PreDive: preserving diversity in test cases for evolving digital circuits using grammatical evolution
Recommendations
Multi-objective black-box test case selection for system testing
GECCO '17: Proceedings of the Genetic and Evolutionary Computation ConferenceTesting is a fundamental task to ensure software quality. Regression testing aims to ensure that changes to software do not introduce new failures. As resources are often limited and testing comprises a vast amount of test cases, different regression ...
Black-box Test Case Selection by Relating Code Changes with Previously Fixed Defects
EASE '22: Proceedings of the 26th International Conference on Evaluation and Assessment in Software EngineeringSoftware continuously changes to address new requirements and to fix defects. Regression testing is performed to ensure that the applied changes do not adversely affect existing functionality. The increasing number of test cases makes it infeasible to ...
Population size reduction for the differential evolution algorithm
This paper studies the efficiency of a recently defined population-based direct global optimization method called Differential Evolution with self-adaptive control parameters. The original version uses fixed population size but a method for gradually ...
Comments