Cloud systems constantly experience changes. Unfortunately, these changes often introduce regression failures, breaking the same features or functionalities repeatedly. Such failures disrupt cloud availability and waste developers' efforts in re-investigating similar incidents. In this position paper, we argue that regression failures can be effectively prevented by enforcing low-level semantics, a new class of intermediate rules empirically inferred from past incidents, yet capable of offering partial correctness guarantees. Our experience shows that such rules are valuable to strengthen system correctness guarantees and expose new bugs.

Systems software today is composed of numerous modules and exhibits complex failure modes. Existing failure detectors focus on catching simple, complete failures and treat programs uniformly at the process level. In this paper, we argue that modern software needs intrinsic failure detectors that are tailored to individual systems and can detect anomalies within a process at finer granularity. We particularly advocate a notion of intrinsic software watchdogs and propose an abstraction for it. Among the different styles of watchdogs, we believe watchdogs that imitate the main program can provide the best combination of completeness, accuracy and localization for detecting gray failures. But, manually constructing such mimic-type watchdogs is challenging and time-consuming. To close this gap, we present an early exploration for automatically generating mimic-type watchdogs.