The high configurability of modern software systems has made configuration tuning a crucial step for assuring system performance, e.g., latency or throughput. However, given the expensive measurements, large configuration space, and rugged configuration landscape, existing tuners suffer ineffectiveness due to the difficult balance of budget utilization between exploring uncertain regions (for escaping from local optima) and exploiting guidance of known good configurations (for fast convergence). The root cause is that we lack knowledge of where the promising regions lay, which also causes challenges in the explainability of the results. In this paper, we propose PromiseTune that tunes the configuration guided by causally purified rules. PromiseTune is unique in the sense that we learn rules, which reflect certain regions in the configuration landscape, and purify them with causal inference. The remaining rules serve as approximated reflections of the promising regions, bounding the tuning to emphasize these places in the landscape. This, as we demonstrate, can effectively mitigate the impact of the exploration and exploitation trade-off. Those purified regions can then be paired with the measured configurations to provide spatial explainability at the landscape level. Compared with 11 state-of-the-art tuners on 12 systems and varying budgets, we show that PromiseTune performs significantly better than the others with \(42\%\) superior rank to the overall second best while providing richer information to explain the hidden system characteristics.

@inproceedings{abs-2507-05995,
  title = {PromiseTune: Unveiling Causally Promising and Explainable Configuration Tuning},
  author = {Pengzhou Chen and Tao Chen},
  booktitle = {ICSE},
  year = {2026},
  doi = {10.1145/3744916.3764552}
}