The "Why/When" of the PDGL
Authors
At this point we know what the PDGL does, deriving a sample of words from a grammar. What we will see here are some examples of why/when this sampling process is useful. The common thread you should notice in these examples is that the PDGL creates large datasets with understanding and control of the space being sampled. During software validation or when undertaking research experimentation this can serve to create lightly or heavily tailored datasets.
Warning:
I'm not an electrical/computer engineer or a biologist. Meaning I don't design logic circuits from gates and I don't work in a wet lab. I have a surface level understanding of processes in these domains. I will give here the story as I understand it, if you find something inactuate let me know.
Validation of Circuit Design in the Very-Large-Scale Integration (VLSI) Process
You probably don't think very much about the fact that your life revolves around rocks. I mean that's caveman stuff right? Granted the rocks we depend on look a lot different from an arrowhead or handaxe. If you're reading this on a computer of some sort you're actively using fancy rocks(I hope you aren't printing out web pages... it's bad for the environment and makes clicking buttons a lot harder). The integrated circuits (ICs) you're using are essentially rocks that we shot with a laser to turn into logic systems.
Unfortunately, just walking into your yard, picking up a piece of quartz, and shooting it with a laser won't magically create an IC. Instead, production of an IC, particularly complex ICs, requires carrying out a complex multistep development process called Very-Large-Scale Integration (VLSI).
An overview of the IC design process1.
Consider how complicated the process of IC creation is and how are critical they are to our lives. Every day when we press the brake pedal in our car we trust in the reliability of ICs. That reliability comes in part from IC verification processes (orange in the figure above). The level of complexity in the VLSI process is extremely high, at this level abstracting test cases becomes easier than direct creation. This abstraction can take the form of a grammar whose words are test cases. As discussed by Maurer 2 this verification abstraction process is one of the motivating applications for the original DGL.
Sampling RNA:DNA Hybrids
Under Construction:
See paper by Jonoska, Obatake, Poznanovic, Price, Riehl, and Vazquez 3.
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Peter M. Maurer. DGL Version 2 – Random Testing in the Mobile Computing Era. In Henry Han and Erich Baker, editors, Next Generation Data Science, volume 2113, pages 172–183. Springer Nature Switzerland, 2024. URL: https://link.springer.com/10.1007/978-3-031-61816-1_12 (visited on 2026-01-16), doi:10.1007/978-3-031-61816-1_12. ↩
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Natasa Jonoska, Nida Obatake, Svetlana Poznanovic, Candice Price, Manda Riehl, and Mariel Vazquez. Modeling RNA:DNA Hybrids with Formal Grammars. In Rebecca Segal, Blerta Shtylla, and Suzanne Sindi, editors, Using Mathematics to Understand Biological Complexity, volume 22, pages 35–54. Springer International Publishing, Cham, 2021. doi:10.1007/978-3-030-57129-0_3. ↩
