The practice of continuous deployment has enabled companies to reduce time-to-market by increasing the rate at which software can be deployed. However, deploying more frequently bears the risk that occasionally defective changes are released. For Internet companies, this has the potential to degrade the user experience and increase user abandonment. Therefore, quality control gates are an important component of the software delivery process. These are used to build confidence in the reliability of a release or change. Towards this end, a common approach is to perform a canary test to evaluate new software under production workloads. Detecting defects as early as possible is necessary to reduce exposure and to provide immediate feedback to the developer.

Large-scale distributed systems must be built to anticipate and mitigate a variety of hardware and software failures. In order to build confidence that fault-tolerant systems are correctly implemented, Netflix (and similar enterprises) regularly run failure drills in which faults are deliberately injected in their production system. The combinatorial space of failure scenarios is too large to explore exhaustively. Existing failure testing approaches either randomly explore the space of potential failures randomly or exploit the "hunches" of domain experts to guide the search. Random strategies waste resources testing "uninteresting" faults, while programmer-guided approaches are only as good as human intuition and only scale with human effort.

In the field of Music Information Retrieval (MIR), multi-label genre classification is the problem of assigning one or more genre labels to a music piece. In this work, we propose a set of ensemble techniques, which are specific to the task of multi-label genre classification. Our goal is to enhance classification performance by combining multiple classifiers. In addition, we also investigate some existing ensemble techniques from machine learning. The effectiveness of these techniques is demonstrated through a set of empirical experiments and various related issues are discussed. To the best of our knowledge, there has been limited work on applying ensemble techniques to multi-label genre classification in the literature and we consider the results in this work as our initial efforts toward this end. The significance of our work has two folds: (1) proposing a set of ensemble techniques specific to music genre classification and (2) shedding light on further research along this direction.