Large Scale Distributed Testing for Fault Classification and Isolation
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Developing confidence in the quality of software is an increasingly difficult problem. As the complexity and integration of software systems increases, the tools and techniques used to perform quality assurance (QA) tasks must evolve with them. To date, several quality assurance tools have been developed to help ensure of quality in modern software, but there are still several limitations to be overcome. Among the challenges faced by current QA tools are (1) increased use of distributed software solutions, (2) limited test resources and constrained time schedules and (3) difficult to replicate and possibly rarely occurring failures. While existing distributed continuous quality assurance (DCQA) tools and techniques, including our own Skoll project, begin to address these issues, new and novel approaches are needed to address these challenges. This dissertation explores three strategies to do this.
First, I present an improved version of our Skoll distributed quality assurance system. Skoll provides a platform for executing sophisticated, long-running QA processes across a large number of distributed, heterogeneous computing nodes. This dissertation details changes to Skoll resulting in a more robust, configurable, and user-friendly implementation for both the client and server components. Additionally, this dissertation details infrastructure development done to support the evaluation of DCQA processes using Skoll -- specifically the design and deployment of a dedicated 120-node computing cluster for evaluating DCQA practices. The techniques and case studies presented in the latter parts of this work leveraged the improvements to Skoll as their testbed.
Second, I present techniques for automatically classifying test execution outcomes based on an adaptive-sampling classification technique along with a case study on the Java Architecture for Bytecode Analysis (JABA) system. One common need for these techniques is the ability to distinguish test execution outcomes (e.g., to collect only data corresponding to some behavior or to determine how often and under which conditions a specific behavior occurs). Most current approaches, however, do not perform any kind of classification of remote executions and either focus on easily observable behaviors (e.g., crashes) or assume that outcomes' classifications are externally provided (e.g., by the users). In this work, I present an empirical study on JABA where we automatically classified execution data into passing and failing behaviors using adaptive association trees.
Finally, I present a long-term case study of the highly-configurable MySQL open-source project. Exhaustive testing of real-world software systems can involve configuration spaces that are too large to test exhaustively, but that nonetheless contain subtle interactions that lead to failure-inducing system faults. In the literature covering arrays, in combination with classification techniques, have been used to effectively sample these large configuration spaces and to detect problematic configuration dependencies. Applying this approach in practice, however, is tricky because testing time and resource availability are unpredictable. Therefore we developed and evaluated an alternative approach that incrementally builds covering array schedules. This approach begins at a low strength, and then iteratively increases strength as resources allow reusing previous test results to avoid duplicated effort. The results are test schedules that allow for successful classification with fewer test executions and that require less test-subject specific information to develop.