The shift-left principle in the industry requires us to test a software application as early as possible. In particular, when code changes in a microservice application are committed to the code repository, we have to efficiently identify all public microservice interfaces affected by the changes so that the impacted interfaces can be tested as soon as possible. However, developing an efficient change impact analysis is extremely challenging in microservices due to the multilingual problem: microservice applications are often implemented using varying programming languages and involve diverse frameworks and configuration files. To address this issue, this paper presents MICROSCOPE, a language-agnostic change impact analysis that uniformly represents code, configuration files, frameworks, and code changes by relational Datalog rules. MICROSCOPE then benefits from an efficient Datalog solver to identify impacted interfaces. Experiments based on the use of MICROSCOPE in Ant Group, a leading software vendor, demonstrate that MICROSCOPE is both effective and fast, as it successfully identifies interfaces affected by 112 code commits, with moderate time overhead, and could reduce 97% of interfaces to test and save 73% of testing time after code changes.

In recent years, security test of network protocols based on fuzzing has been attracting more and more attentions. This is very challenging compared with the stateless software fuzzing and most early network protocol fuzzers are of low speed and poor test effect. Since the first greybox and stateful fuzzer named AFLNET was proposed, several new schemes have been designed to improve its performance from different aspects. During the research, a great challenge is how to greatly improve the fuzzing efficiency. Based on the basic analysis in SNPSFuzzer, this paper provides a more thorough analysis about the time consumption in a fuzzing iteration for 13 network protocols and then we design a High-speed Network Protocol Fuzzer named HNPFuzzer. In HNPFuzzer, the test cases and response messages between the client and server are transmitted through the shared memory, guided by a precise synchronizer, rather than the socket interfaces. This greatly shorten the period of an iteration. Moreover, we design a persistent mode attempting to fuzz the service instances in the memory more than one time based on analyzing the side effect information. This mode further improves the speed of fuzzing. Experiment results illustrate that our scheme can improve the fuzzing throughput by about 39.66 times in average and triggers a large number of crashes including 2 new vulnerabilities which cannot discovered by existing fuzzers. Note that, the existing network protocol fuzzing schemes proposed in different directions do not compete with each other and on the contrary, they can collaborate with each other to improve the overall fuzzing effect and efficiency. Consequently, more existing tools can be integrated into our framework to get better network protocol fuzzing effect.

In this study, as part of an effort to study snowfall characteristics and quantify winter precipitation in East China, we investigated the microphysical properties of snowfall, including size, shape, density, and terminal velocity using a 2-D video disdrometer (2-DVD) and a weighing precipitation gauge in Nanjing (NJ), East China during the winters of 2015-2019. We obtained larger snow density and terminal velocity values than those reported in the literature for this region. Higher snow density could account for higher snowflake terminal velocity, after removing the effects of observation altitude and surface temperature. We then fit the snow particle size distributions (PSDs) to the gamma model and explored the interrelationships among the model parameters and snowfall rate (SR). The relationship between radar reflectivity factor (Ze) and SR was derived based on snow PSD measurements and the snow density relation. Using this Ze-SR relationship, the estimated liquid-equivalent SRs are obtained from S-band NJ radar data collected during several snowfall events. Radar-inferred SRs showed reasonable agreement with those measured on the ground, with a mean absolute error of 16% for the collected snowfall events in NJ.