Abstract-Automated Static Analysis Tools (ASATs) are an integral part of today's software quality assurance practices. At present, a plethora of ASATs exist, each with different strengths. However, there is little guidance for developers on which of these ASATs to choose and combine for a project. As a result, many projects still only employ one ASAT with practically no customization. With UAV, the Unified ASAT Visualizer, we created an intuitive visualization that enables developers, researchers, and tool creators to compare the complementary strengths and overlaps of different Java ASATs. UAV's enriched treemap and source code views provide its users with a seamless exploration of the warning distribution from a high-level overview down to the source code. We have evaluated our UAV prototype in a user study with ten second-year Computer Science (CS) students, a visualization expert and tested it on large Java repositories with several thousands of PMD, FindBugs, and Checkstyle warnings. Project Website: https://clintoncao.github.io/uav/

These days more companies are shifting towards using cloud environments to provide their services to their client. While it is easy to set up a cloud environment, it is equally important to monitor the system's runtime behaviour and identify anomalous behaviours that occur during its operation. In recent years, the utilisation of Recurrent Neural Networks (RNNs) and Deep Neural Networks (DNNs) to detect anomalies that might occur during runtime has been a trending approach. However, it is unclear how to explain the decisions made by these networks and how these networks should be interpreted to understand the runtime behaviour that they model. On the contrary, state machine models provide an easier manner to interpret and understand the behaviour that they model. In this work, we propose an approach that learns state machine models to model the runtime behaviour of a cloud environment that runs multiple microservice applications. To the best of our knowledge, this is the first work that tries to apply state machine models to microservice architectures. The state machine model is used to detect the different types of attacks that we launch on the cloud environment. From our experiment results, our approach can detect the attacks very well, achieving a balanced accuracy of 99.2% and a 𝐹 1 score of 0.982. CCS CONCEPTS• Computing methodologies → Anomaly detection.

NetFlow data is a well-known network log format used by many network analysts and researchers. The advantages of using this format compared to pcap are that it contains fewer data, is less privacy intrusive, and is easier to collect and process. However, having less data does mean that this format might not be able to capture important network behaviour as all information is summarised into statistics. Much research aims to overcome this disadvantage through the use of machine learning, for instance, to detect attacks within a network. Many approaches can be used to pre-process the NetFlow data before it is used to train the machine learning algorithms. However, many of these approaches simply apply existing methods to the data, not considering the specific properties of network data. We argue that for data originating from software systems, such as NetFlow or software logs, similarities in frequency and contexts of feature values are more important than similarities in the value itself. In this work, we, therefore, propose an encoding algorithm that directly takes the frequency and the context of the feature values into account when the data is being processed. Different types of network behaviours can be clustered using this encoding, thus aiding the process of detecting anomalies within the network. From windows of these clusters obtained from monitoring a clean system, we learn state machine behavioural models for anomaly detection. These models are very well-suited to modelling the cyclic and repetitive patterns present in NetFlow data. We evaluate our encoding on a new dataset that we created for detecting problems in Kubernetes clusters and on two wellknown public NetFlow datasets. The obtained performance results of the state machine models are comparable to existing works that use many more features and require both clean and infected data as training input.