LUCIANO BERIO’S NATURALE: A STORY COMBINING FOLK MUSIC AND ART MUSIC

The study of a score by a serious performer is a fundamental step in the process of arriving at a knowledgeable and deeply informed approach to performing a piece of music. In order to obtain this knowledge numerous aspects of the score must be taken into consideration. It is the intent of this dissertation to gather and analyze the information concerning Naturale, a work written by Luciano Berio in 1985 for viola, percussion and recorded voice, based on Sicilian folk songs. All the aspects surrounding Naturale’s existence are taken into consideration in this study. First, it is important to reflect on Berio’s compositional style and traits, the manner in which he relates his works one to another, what he sees in folk music and his own personal desire to intertwine art music and folk music. For Berio Naturale is not an isolated venture into the realm of mixing folk music and his own avant-garde style; it is instead one of many works resulting from his long-standing relationship with folk music. Another essential aspect in this case is the study of Sicilian folk music itself, and the sources used by Berio to find the songs by which he was inspired. The work is examined section by section with figures showing both excerpts of Naturale as well as the original songs with their translations. An analysis containing harmonic, thematic and formal aspects of the score was developed in order to arrive at a better understanding of the structure and pacing of the piece. For this research the author went to Italy to conduct an interview with Maestro Aldo Bennici, the Sicilian violist for whom Naturale was composed. This interview helped in the discovery of two more songs used by Berio that have not to this point been identified in any other document. Bennici’s outstanding testimony portrayed the expressive character of this music and the evocative imagery behind this score. I hope to bring this knowledge to other performers, that they may fully understand and appreciate the unique beauty and power of Berio’s Naturale.

FRAMEWORK SYNTHESIS FOR SYMBOLIC EXECUTION OF EVENT-DRIVEN FRAMEWORKS

Symbolic execution is a powerful program analysis technique, but it is very challenging to apply to programs built using event-driven frameworks, such as Android. The main reason is that the framework code itself is too complex to symbolically execute. The standard solution is to manually create a framework model that is simpler and more amenable to symbolic execution. However, developing and maintaining such a model by hand is difficult and error-prone. We claim that we can leverage program synthesis to introduce a high-degree of automation to the process of framework modeling. To support this thesis, we present three pieces of work. First, we introduced SymDroid, a symbolic executor for Android. While Android apps are written in Java, they are compiled to Dalvik bytecode format. Instead of analyzing an app’s Java source, which may not be available, or decompiling from Dalvik back to Java, which requires significant engineering effort and introduces yet another source of potential bugs in an analysis, SymDroid works directly on Dalvik bytecode. Second, we introduced Pasket, a new system that takes a first step toward automatically generating Java framework models to support symbolic execution. Pasket takes as input the framework API and tutorial programs that exercise the framework. From these artifacts and Pasket's internal knowledge of design patterns, Pasket synthesizes an executable framework model by instantiating design patterns, such that the behavior of a synthesized model on the tutorial programs matches that of the original framework. Lastly, in order to scale program synthesis to framework models, we devised adaptive concretization, a novel program synthesis algorithm that combines the best of the two major synthesis strategies: symbolic search, i.e., using SAT or SMT solvers, and explicit search, e.g., stochastic enumeration of possible solutions. Adaptive concretization parallelizes multiple sub-synthesis problems by partially concretizing highly influential unknowns in the original synthesis problem. Thanks to adaptive concretization, Pasket can generate a large-scale model, e.g., thousands lines of code. In addition, we have used an Android model synthesized by Pasket and found that the model is sufficient to allow SymDroid to execute a range of apps.