COMPARING AUTOMATED UNIT TESTING STRATEGIES

Software testing plays a:critical role in the software development lifecycle. Auto­ mated unit testing strategies allow a tester to execute a large number of test cases to detect faulty behaviours in a piece of software. Many different automated unit testing strategies can be applied to test a program. In order to better understand the relationship between these strategies, “explorative” strategies are defined as those which select unit tests by exploring a large search space with a relatively simple data structure. This thesis focuses on comparing three particular explorative strategies: bounded-exhaustive, randomized, and a combined strategy. In order to precisely compare these three strategies, a test program is developed to provide a universal framework for generating and executing test cases. The test program implements the three strategies as well. In addition, we perform several experiments on these three strategies using the test program. The experimental data is collected and analyzed to illustrate the relationship between these strategies

LeCo: Lightweight Compression via Learning Serial Correlations

Lightweight data compression is a key technique that allows column stores to exhibit superior performance for analytical queries. Despite a comprehensive study on dictionary-based encodings to approach Shannon's entropy, few prior works have systematically exploited the serial correlation in a column for compression. In this paper, we propose LeCo (i.e., Learned Compression), a framework that uses machine learning to remove the serial redundancy in a value sequence automatically to achieve an outstanding compression ratio and decompression performance simultaneously. LeCo presents a general approach to this end, making existing (ad-hoc) algorithms such as Frame-of-Reference (FOR), Delta Encoding, and Run-Length Encoding (RLE) special cases under our framework. Our microbenchmark with three synthetic and six real-world data sets shows that a prototype of LeCo achieves a Pareto improvement on both compression ratio and random access speed over the existing solutions. When integrating LeCo into widely-used applications, we observe up to 3.9x speed up in filter-scanning a Parquet file and a 16% increase in Rocksdb's throughput

Deep reinforcement learning on 1-layer circuit routing problem

In VLSI design, routing is the step that determines the paths for circuit nets and interconnections. While routing can be a very complex process involving time, congestion and space information, the problem can be modelled as a maze routing problem. In specific, given a 2d array and a set of start nodes and end nodes, the agent is trying to optimize the solution by connectivity and path length. Traditionally, the routing problem is solved using graph search techniques such as Lee’s algorithm. The result produced by graph search algorithms relies heavily on the order of routing. While some simple heuristics are available, the result is not stable because simple heuristics take greedy approaches and neglect the long-term reward. The recent development of deep learning, especially deep reinforcement learning, can be a good approach to finding better ordering on attacking the routing problem. We introduce a reinforcement learning approach to the traditional 2-point nets in 1-layer maze routing problem

Valley-Hall photonic topological insulators with dual-band kink states

Extensive researches have revealed that valley, a binary degree of freedom (DOF), can be an excellent candidate of information carrier. Recently, valley DOF has been introduced into photonic systems, and several valley-Hall photonic topological insulators (PTIs) have been experimentally demonstrated. However, in the previous valley-Hall PTIs, topological kink states only work at a single frequency band, which limits potential applications in multiband waveguides, filters, communications, and so on. To overcome this challenge, here we experimentally demonstrate a valley-Hall PTI, where the topological kink states exist at two separated frequency bands, in a microwave substrate-integrated circuitry. Both the simulated and experimental results demonstrate the dual-band valley-Hall topological kink states are robust against the sharp bends of the internal domain wall with negligible inter-valley scattering. Our work may pave the way for multi-channel substrate-integrated photonic devices with high efficiency and high capacity for information communications and processing

Realization of a three-dimensional photonic topological insulator

Confining photons in a finite volume is in high demand in modern photonic devices. This motivated decades ago the invention of photonic crystals, featured with a photonic bandgap forbidding light propagation in all directions. Recently, inspired by the discoveries of topological insulators (TIs), the confinement of photons with topological protection has been demonstrated in two-dimensional (2D) photonic structures known as photonic TIs, with promising applications in topological lasers and robust optical delay lines. However, a fully three-dimensional (3D) topological photonic bandgap has never before been achieved. Here, we experimentally demonstrate a 3D photonic TI with an extremely wide (> 25% bandwidth) 3D topological bandgap. The sample consists of split-ring resonators (SRRs) with strong magneto-electric coupling and behaves as a 'weak TI', or a stack of 2D quantum spin Hall insulators. Using direct field measurements, we map out both the gapped bulk bandstructure and the Dirac-like dispersion of the photonic surface states, and demonstrate robust photonic propagation along a non-planar surface. Our work extends the family of 3D TIs from fermions to bosons and paves the way for applications in topological photonic cavities, circuits, and lasers in 3D geometries