The design of irregular subarrayed planar phased arrays composed of modular tiles is a research area of increasing interest. In this article, a heuristic iterative convex relaxation programming (H-ICRP) framework is proposed. By formulating the tiling of irregular subarray as a binary programming problem, this framework is suitable for arbitrary array aperture and subarray structure. To achieve exact aperture cover, the nonconvex binary constraint is relaxed to a convex approximation and solved through multiple iterations. Furthermore, for better performance of the scanning radiation pattern, the multitask Bayesian compressive sensing (MT-BCS) solver is adopted to find a heuristic initial iteration point instead of randomizing, such that the robustness and effectiveness can be improved remarkably. Good performance and potentialities of the proposed method are verified by comparing with existing approaches in various numerical examples.

With the rapid increase of public code repositories, developers maintain a great desire to retrieve precise code snippets by using natural language. Despite existing deep learning-based approaches that provide end-to-end solutions (i.e., accept natural language as queries and show related code fragments), the performance of code search in the large-scale repositories is still low in accuracy because of the code representation (e.g., AST) and modeling (e.g., directly fusing features in the attention stage). In this paper, we propose a novel learnable de ep G raph for C ode S earch (called deGraphCS ) to transfer source code into variable-based flow graphs based on an intermediate representation technique, which can model code semantics more precisely than directly processing the code as text or using the syntax tree representation. Furthermore, we propose a graph optimization mechanism to refine the code representation and apply an improved gated graph neural network to model variable-based flow graphs. To evaluate the effectiveness of deGraphCS , we collect a large-scale dataset from GitHub containing 41,152 code snippets written in the C language and reproduce several typical deep code search methods for comparison. The experimental results show that deGraphCS can achieve state-of-the-art performance and accurately retrieve code snippets satisfying the needs of the users.

Static analysis (SA) tools can generate useful static warnings to reveal the problematic code snippets in a software system without dynamically executing the corresponding source code. In the literature, static warnings are of paramount importance because they can easily indicate specific types of software defects in the early stage of a software development process, which accordingly reduces the maintenance costs by a substantial margin. Unfortunately, due to the conservative approximations of such SA tools, a large number of false positive (FP for short) warnings (i.e., they do not indicate real bugs) are generated, making these tools less effective. During the past two decades, therefore, many false positive mitigation (FPM for short) approaches have been proposed so that more accurate and critical warnings can be delivered to developers. This paper offers a detailed survey of research achievements on the topic of FPM. Given the collected 130 surveyed papers, we conduct a comprehensive investigation from five different perspectives. First, we reveal the research trends of this field. Second, we classify the existing FPM approaches into five different types and then present the concrete research progress. Third, we analyze the evaluation system applied to examine the performance of the proposed approaches in terms of studied SA tools, evaluation scenarios, performance indicators, and collected datasets, respectively. Fourth, we summarize the four types of empirical studies relating to SA warnings to exploit the insightful findings that are helpful to reduce FP warnings. Finally, we sum up 10 challenges unresolved in the literature from the aspects of systematicness, effectiveness, completeness, and practicability and outline possible research opportunities based on three emerging techniques in the future.

Subarray design technique is widely employed in the design of planar phased arrays. However, it's hard to obtain full coverage and good radiation performance with the given shape of subarrays and array aperture. In this letter, the subarray design problem is formulated as an iterative convex relaxation model and solved through some steps subtly. Besides, the scanning sidelobe level (SSL) is adopted to retrieve the optimal configuration among the found solutions. Numerical experiments are carried out to assess the performance of the proposed method as well as the inherent superiority in adaptive beamforming.

Bounded model checking is among the most efficient techniques for the automatic verification of concurrent programs. However, due to the nondeterministic thread interleavings, a huge and complex formula is usually required to give an exact encoding of all possible behaviors, which significantly limits the salability. Observing that the huge formula is usually dominated by the exact encoding of the scheduling constraint, this paper proposes a novel scheduling constraint based abstraction refinement method for multi-threaded C program verification. Our method is both efficient in practice and complete in theory, which is challenging for existing techniques. To achieve this, we first proposed an effective and powerful technique which works well for nearly all examples in practice. We have proposed the notion of Event Order Graph (EOG), and have devised two graph-based algorithms over EOG for counterexample validation and refinement generation, which can always obtain a small yet effective refinement constraint. Then, to ensure the completeness, our method was enhanced with two constraint-based algorithms for counterexample validation and refinement generation. Experimental results on SV-COMP 2017 benchmarks and two real-world server systems indicate that our method is promising and significantly outperforms the existing state-of-the-art tools.