PREDICTING FAULT-PRONE MODULES IN EMBEDDED SYSTEMS USING ANALOGY-BASED CLASSIFICATION MODELS
Bibliographic record
Abstract
Embedded systems have become ubiquitous and essential entities in our ever growing high-tech world. The backbone of today's information-highway infrastructure are embedded systems such as telecommunication systems. They demand high reliability, so as to prevent severe consequences of failures including costly repairs at remote sites. Technology changes mandate that embedded systems evolve, resulting in a demand for techniques for improving reliability of their future system releases. Reliability models based on software metrics can be effective tools for software engineering of embedded systems, because quality improvements are so resource-consuming that it is not feasible to apply them to all modules. Identification of the likely fault-prone modules before system testing, can be effective in reducing the likelihood of faults discovered during operations. A software quality classification model is calibrated using software metrics from a past release, and is then applied to modules currently under development to estimate which modules are likely to be fault-prone. This paper presents and demonstrates an effective case-based reasoning approach for calibrating such classification models. It is attractive for software engineering of embedded systems, because it can be used to develop software reliability models using a faster, cheaper, and easier method. We illustrate our approach with two large-scale case studies obtained from embedded systems. They involve data collected from telecommunication systems including wireless systems. It is indicated that the level of classification accuracy observed in both case studies would be beneficial in achieving high software reliability of subsequent releases of the embedded systems.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".