Towards an Automated Approach for Testing IoT Devices
Bibliographic record
Abstract
The Internet of Things (IoT) comprises a network of physical devices embedded with sensors and software to collect and exchange data with other devices and systems via the Internet. IoT devices vary from small devices to complex industrial appliances. Despite the increase in the number of IoT devices, there is a lack of proper testing for these devices, which can impact the functionality of IoT systems. This study focuses on an automated approach for testing IoT systems that use Android-based devices. We propose an approach to generate test cases (TCs) and execute them in physical devices as instrumented tests. Our methodology uses source code as input. We analyze the source code and create an Abstract Syntax Tree (AST). We navigate the AST to identify classes, methods, input parameters, and return types. We manually create a test case (TC) template and use a heuristic search algorithm to generate the test data for each unit test. We populate the TC templates with information extracted from the AST and data generated by a heuristic search algorithm to generate executable TCs. We assess the quality of generated TCs using the mutation analysis technique. The experiment demonstrates that the proposed approach effectively generates executable TCs for conducting functional tests for IoT devices. This study can be beneficial for practitioners, researchers, and device manufacturers towards improvement in the way IoT devices are tested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".