Peephole optimization is an essential class of compiler optimizations that targets small, inefficient instruction sequences within programs. By replacing such suboptimal instructions with refined and more optimal sequences, these optimizations not only directly optimize code size and performance, but also enable more transformations in the subsequent optimization pipeline. Despite their importance, discovering new and effective peephole optimizations remains challenging due to the complexity and breadth of instruction sets. Prior approaches either lack scalability or have significant restrictions on the peephole optimizations that they can find.

This paper proposes a joint beamforming design for optimizing integrated sensing and communication (ISAC) systems under imperfect channel state information (CSI), leveraging a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS). Owing to the deployment of large-scale antenna arrays and high carrier frequencies, the Rayleigh distance can extend to tens or even hundreds of meters. This expansion leads to a fundamental paradigm shift in electromagnetic field characteristics, transitioning from the conventional far-field regime to the emerging near-field regime. As a result, the propagation characteristics experienced by users and the target may differ. Accordingly, we consider a practical scenario in which users and the target are situated in distinct fields. However, this hybrid-field model increases the system’s sensitivity to channel estimation errors (CEE). To this end, we propose a robust design that jointly optimizes beamforming for base station (BS) and STAR-RIS, aiming to maximize the achievable sum-rate of the nodes while satisfying the constraint of sensing requirements. Under a statistical CEE model, we derive the interference covariance matrix and reformulate the maximization problem as an equivalent weighted mean square error (MSE) minimization problem. Subsequently, the transformed problem is decoupled into multiple sub-problems using a block coordinate descent (BCD)-based algorithm. The algorithm capitalizes on semidefinite relaxation and Gaussian randomization to obtain an effective solution. Finally, simulation results validate the effectiveness of the proposed robust design. Compared with the baseline schemes, the proposed algorithm achieves a higher communication sum-rate and illustrates how various parameters affect the performance.

Delta Debugging is a widely used family of algorithms (e.g., ddmin and ProbDD) to automatically minimize bug-triggering test inputs, thus to facilitate debugging. It takes a list of elements with each element representing a fragment of the test input, systematically partitions the list at different granularities, identifies and deletes bug-irrelevant partitions. Prior delta debugging algorithms assume there are no differences among the elements in the list, and thus treat them uniformly during partitioning. However, in practice, this assumption usually does not hold, because the size (referred to as weight) of the fragment represented by each element can vary significantly. For example, a single element representing 50% of the test input is much more likely to be bug-relevant than elements representing only 1%. This assumption inevitably impairs the efficiency or even effectiveness of these delta debugging algorithms. This paper proposes Weighted Delta Debugging (WDD), a novel concept to help prior delta debugging algorithms overcome the limitation mentioned above. The key insight of WDD is to assign each element in the list a weight according to its size, and distinguish different elements based on their weights during partitioning. We designed two new minimization algorithms,$\mathbf{W}_{\text{ddmin}}$and$\mathbf{W}_{\text{ProbDD }}$, by applying WDD to ddmin and ProbDD respectively. We extensively evaluated$\mathbf{W}_{\text{ddmin}}$and$\mathbf{W}_{\text{ProbDD }}$in two representative applications, HDD and Perses, on 62 benchmarks across two languages. On average, with$\mathbf{W}_{\text{ddmin }}$, HDD and Perses took 51.31% and 7.47% less time to generate 9.12% and 0.96% smaller results than with ddmin, respectively. With$\mathbf{W}_{\text{ProbDD }}$, HDD and Perses used 11.98% and 9.72% less time to generate 13.40% and 2.20% smaller results than with ProbDD, respectively. The results strongly demonstrate the value of WDD. We firmly believe that WDD opens up a new dimension to improve test input minimization techniques.