Diagnosing performance issues is a slow and labor-intensive process, especially for modern complex microservice systems. In this paper, we propose a novel performance diagnosis framework for microservice application based on intra-/inter-trace analysis. For a slow request to be diagnosed, our approach first determines whether the anomaly is caused by on-path service by aggregating and comparing normal/abnormal traces. Considering that some performance problems may actually be caused by off-path services which do not lie in the path of the abnormal traces, our approach further identifies traces which have temporal relation with the abnormal trace by proposed inter-trace analysis technique, and locates the root cause based on traffic analysis.

Web applications play an important role in modern society. Quality assurance of web applications requires lots of manual efforts. In this paper, we propose WebQT, an automatic test case generator for web applications based on reinforcement learning. Specifically, to increase testing efficiency, we design a new reward model, which encourages the agent to mimic human testers to interact with the web applications. To alleviate the problem of state redundancy, we further propose a novel state abstraction technique, which can identify different web pages with the same functionality as the same state, and yields a simplified state space. We evaluate WebQT on seven open-source web applications. The experimental results show that WebQT achieves 45.4% more code coverage along with higher efficiency than the state-of-the-art technique. In addition, WebQT also reveals 69 exceptions in 11 real-world web applications.

Regression testing is an important means of assessing the quality of Node.js applications. However, non-deterministic executions inside Node.js framework could make test cases intermittently pass or fail on the same version of code, which are called flaky tests. Flaky tests can cause unreliable test results, and make developers waste a significant amount of time debugging the bugs that do not belong to the target application. In this paper, we conduct an empirical study on 87 flaky tests from 7 popular Node.js applications, and analyze the non-determinism that causes these flaky tests. Through this study, there is a wide range of non-determinism to cause flaky tests, including non-deterministic event triggering order, non-deterministic function calls, non-deterministic process/thread scheduling order, non-deterministic execution of asynchronous tasks and non-deterministic event triggering data. The result reveals that, existing approaches on event race detection are not sufficient for flaky test detection. In future, researchers can design flaky test detection approaches targeted at different categories of non-determinism.

With the increasing number of browsers and platforms on which the applications can be executed, cross-browser incompatibilities (XBIs) are becoming a serious problem for organizations to develop web-based software. In order to eliminate cross browser issues, existing work tries to repair layout XBIs using search-based technique. However, the designed fitness function is too strict, and may miss the chance to repair some layout XBIs. For each reported layout XBI, it will search all possible candidate fixes, and cannot reuse existing repair solutions for similar issues. This paper proposes to combine learning and search based technique to improve the state-of-the-art of automated repair of layout XBIs. By extracting the characteristics of successfully repaired layout XBIs and learn a decision tree, our approach will accelerate the search process by directly applying the recommended solution to fix similar XBIs. Moreover, by redesigning search-based repair process, our technique can improve the chance to repair layout XBIs. The initial evaluation shows that the proposed approach is effective.

Regression testing is an expensive activity in software development. To speed it up, regression test selection (RTS) is a promising approach by selecting a subset of tests which are affected by code changes. Although there are lots of regression test selection tools, most of them aim to unit tests, require direct code dependency between tests and code under test, and cannot be applied to Web applications to select end-to-end web tests. This paper presents WebRTS, a dynamic RTS tool for regression testing of Web applications. By tracking the process of Http request and object construction in the server, WebRTS can collect accurate test dependencies for each test in isolation, and supports parallel regression testing of distributed Web application. The design of WebRTS is also flexible, and it can be combined with different web testing frameworks. The experimental results show that WebRTS is effective and can be used to select regression tests for Java Web applications. Video: https://youtu.be/OlAsvrX7HXc. Source code: https://gitlab.com/aozeliu18/webrts.

Modern distributed applications are moving toward a microservice architecture, in which each service is developed and managed independently, and new features and updates are delivered continuously. A guiding principle of microservice architecture is that it is vital to anticipate and mitigate a variety of hardware and software failures. In order to test the fault handling capabilities of microservices automatically, this paper presents IntelliFT, a guided resilience testing technique for microservice based applications, which aims to expose the defects in the fault-handling logic effectively within a fixed time limit. The characteristic of IntelliFT is that it leverages existing integration tests of the applications under test to explore the fault space, and decides whether injected faults can lead to severe failures by designing fitness-guided search technique. Our experimental results on a medium-size microservice benchmark system show that the proposed technique is effective, improves the state-of-the-art, and can quickly expose bugs in the recovery logic.

Record-and-replay tools are important for quality assurance of Web applications by capturing user case scenarios and executing them automatically when needed. However, the tests generated by existing techniques are brittle, and often lead to test breakages as the dynamic behavior and frequent updates of modern Web applications. In this paper, we propose WebRR, a self-replay enhanced robust record-and-replay technique for Web applications testing. The novelty of WebRR is that, it introduces a new self-replay mechanism in the recording phase, which checks the captured event from the record module online, and generates multiple locators (including DOM locators, visual locator and proximity locators) automatically, to improve the robustness of generated test cases. During the replay, it combines multiple locators and new local workflow repair technique to repair test breakages, and can improve the resilience of generated tests to frequent updates of the applications. We applied our approach to 3 enterprise Web applications, which are deployed in a large power grid company of China. The experimental results show that WebRR is effective, and substantially improve the robustness of end-to-end web tests that are generated using record-and-replay technique.

The adoption of radio frequency identification (RFID) tags and devices facilitates the observation and monitoring of event patterns, while highly temporal restrained RFID data further make events complicated. Although the existing Pub/Sub systems can help distributed applications monitor and disseminate interesting events, they are unable to express RFID-related events directly. Extending a system for new RFID requirements not only needs a lot of work, but also provides no performance guarantees. It is, therefore, necessary to design and implement an effective and efficient Pub/Sub system in order to capture and disseminate RFID-related events. This paper presents a composite subscription specification for RFID-related application scenarios. It enables the subscription of predicates for RFID code as well as various complex events. Moreover, this paper proposes the algorithms for RFID-code subscription matching and complex events detection which have been implemented in our Pub/Sub system, OncePubSub. Also, experiments were conducted to quantify the performance and overhead of the above algorithms. Performance evaluation results indicate that OncePubSub is more efficient than SIENA- and JESS-based systems.

RFID technology facilitates monitoring and managing products. In most RFID applications, users pay much attention to temporal and logical event patterns, which is further strengthened by highly temporal RFID data. If RFID events or other kinds of events are disseminated to users through Pub/Sub service, visibility of product states will be greatly increased and fast processing these events will become possible. However, the traditional Pub/Sub systems cannot support composite events involving abundant temporal constraints. Therefore, they cannot fully meet the needs of RFID applications. Aiming at RFID-related application scenarios, this paper designs a composite subscription language embedded with diversified temporal operators and proposes a matching algorithm for detecting composite events which has been implemented in our Pub/Sub system OncePubSub. Performance evaluation results indicate our algorithm is more efficient than the two systems based on Siena and JESS respectively.