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  1. Due to the importance of Android app quality assurance, many Android UI testing tools have been developed by researchers over the years. However, recent studies show that these tools typically achieve low code coverage on popular industrial apps. In fact, given a reasonable amount of run time, most state-of-the-art tools cannot even outperform a simple tool, Monkey, on popular industrial apps with large codebases and sophisticated functionalities. Our motivating study finds that these tools perform two types of operations, UI Hierarchy Capturing (capturing information about the contents on the screen) and UI Event Execution (executing UI events, such as clicks), often inefficiently using UIAutomator, a component of the Android framework. In total, these two types of operations use on average 70% of the given test time. Based on this finding, to improve the effectiveness of Android testing tools, we propose TOLLER, a tool consisting of infrastructure enhancements to the Android operating system. TOLLER injects itself into the same virtual machine as the app under test, giving TOLLER direct access to the app’s runtime memory. TOLLER is thus able to directly (1) access UI data structures, and thus capture contents on the screen without the overhead of invoking the Android frameworkmore »services or remote procedure calls (RPCs), and (2) invoke UI event handlers without needing to execute the UI events. Compared with the often-used UIAutomator, TOLLER reduces average time usage of UI Hierarchy Capturing and UI Event Execution operations by up to 97% and 95%, respectively. We integrate TOLLER with existing state-of-the-art/practice Android UI testing tools and achieve the range of 11.8% to 70.1% relative code coverage improvement on average. We also find that TOLLER-enhanced tools are able to trigger 1.4x to 3.6x distinct crashes compared with their original versions without TOLLER enhancement. These improvements are so substantial that they also change the relative competitiveness of the tools under empirical comparison. Our findings highlight the practicality of TOLLER as well as raising the community awareness of infrastructure support’s significance beyond the community’s existing heavy focus on algorithms.« less
  2. Tests that modify (i.e., "pollute") the state shared among tests in a test suite are called \polluter tests". Finding these tests is im- portant because they could result in di erent test outcomes based on the order of the tests in the test suite. Prior work has proposed the PolDet technique for nding polluter tests in runs of JUnit tests on a regular Java Virtual Machine (JVM). Given that Java PathFinder (JPF) provides desirable infrastructure support, such as systematically exploring thread schedules, it is a worthwhile attempt to re-implement techniques such as PolDet in JPF. We present a new implementation of PolDet for nding polluter tests in runs of JUnit tests in JPF. We customize the existing state comparison in JPF to support the so-called \common-root iso- morphism" required by PolDet. We find that our implementation is simple, requiring only -200 lines of code, demonstrating that JPF is a sophisticated infrastructure for rapid exploration of re-search ideas on software testing. We evaluate our implementation on 187 test classes from 13 Java projects and nd 26 polluter tests. Our results show that the runtime overhead of PolDet@JPF com- pared to base JPF is relatively low, on average 1.43x. However, our experimentsmore »also show some potential challenges with JPF.« less
  3. Regression testing is increasingly important with the wide use of continuous integration. A desirable requirement for regression testing is that a test failure reliably indicates a problem in the code under test and not a false alarm from the test code or the testing infrastructure. However, some test failures are unreliable, stemming from flaky tests that can non- deterministically pass or fail for the same code under test. There are many types of flaky tests, with order-dependent tests being a prominent type. To help advance research on flaky tests, we present (1) a framework, iDFlakies, to detect and partially classify flaky tests; (2) a dataset of flaky tests in open-source projects; and (3) a study with our dataset. iDFlakies automates experimentation with our tool for Maven-based Java projects. Using iDFlakies, we build a dataset of 422 flaky tests, with 50.5% order-dependent and 49.5% not. Our study of these flaky tests finds the prevalence of two types of flaky tests, probability of a test-suite run to have at least one failure due to flaky tests, and how different test reorderings affect the number of detected flaky tests. We envision that our work can spur research to alleviate the problem of flakymore »tests.« less