Supporting Robotic Software Migration Using Static Analysis and\n Model-Driven Engineering
Bibliographic record
Abstract
The wide use of robotic systems contributed to developing robotic software\nhighly coupled to the hardware platform running the robotic system. Due to\nincreased maintenance cost or changing business priorities, the robotic\nhardware is infrequently upgraded, thus increasing the risk for technology\nstagnation. Reducing this risk entails migrating the system and its software to\na new hardware platform. Conventional software engineering practices such as\ncomplete re-development and code-based migration, albeit useful in mitigating\nthese obsolescence issues, they are time-consuming and overly expensive. Our\nRoboSMi model-driven approach supports the migration of the software\ncontrolling a robotic system between hardware platforms. First, RoboSMi\nexecutes static analysis on the robotic software of the source hardware\nplatform to identify platform-dependent and platform-agnostic software\nconstructs. By analysing a model that expresses the architecture of robotic\ncomponents on the target platform, RoboSMi establishes the hardware\nconfiguration of those components and suggests software libraries for each\ncomponent whose execution will enable the robotic software to control the\ncomponents. Finally, RoboSMi through code-generation produces software for the\ntarget platform and indicates areas that require manual intervention by robotic\nengineers to complete the migration. We evaluate the applicability of RoboSMi\nand analyse the level of automation and performance provided from its use by\nmigrating two robotic systems deployed for an environmental monitoring and a\nline following mission from a Propeller Activity Board to an Arduino Uno.\n
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".