Is Model Instability just Noise to be Tolerated or a Property that can be Managed?
In software analytics, rerunning the same analysis twice often yields different models and conclusions. This reduces trust in the model and limits its use. We find that model instability is a major problem. Across 127 multi-objective SE optimization problems (12,700 test cases), repeated runs of a state-of-the-art optimizer agree on only 13.7% of test cases, even under improved settings. We argue that this instability is not merely noise to tolerate, but a property that can be measured and managed. By adjusting how labels are spent, how complex the models become, and how splits are scored, we obtain models that agree 4.8 times as often as the default configuration. The standard deviation ...
| Authors | Amirali Rayegan, Lunxiao Li, Tim Menzies |
|---|---|
| Primary category | cs.SE |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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