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- All Subjects: Python (Computer program language)
- Creators: Bazzi, Rida
- Creators: Davey, Connor
- Member of: Theses and Dissertations
- Resource Type: Text
Description
\English is a programming language, a method of allowing programmers to write instructions such that a computer may understand and execute said instructions in the form of a program. Though many programming languages exist, this particular language is designed for ease of development and heavy optimizability in ways that no other programming language is. Building on the principles of Assembly level efficiency, referential integrity, and high order functionality, this language is able to produce extremely efficient code; meanwhile, programmatically defined English-based reusable syntax and a strong, static type system make \English easier to read and write than many existing programming languages. Its generalization of all language structures and components to operators leaves the language syntax open to project-specific syntactical structuring, making it more easily applicable in more cases. The thesis project requirements came in three parts: a compiler to compile \English code into NASM Assembly to produce a final program product; a standard library to define many of the basic operations of the language, including the creation of lists; and C translation library that would utilize \English properties to compile C code using the \English compiler. Though designed and partially coded, the compiler remains incomplete. The standard library, C translation library, and design of the language were completed. Additional tools regarding the language design and implementation were also created, including a Gedit syntax highlighting configuration file; usage documentation describing in a tutorial style the basic usage of the language; and more. Though the thesis project itself may be complete, the \English project will continue in order to produce a new language capable of the abilities possible with the design of this language.
ContributorsDavey, Connor (Author) / Gupta, Sandeep (Thesis director) / Bazzi, Rida (Committee member) / Calliss, Debra (Committee member) / Barrett, The Honors College (Contributor)
Created2016-05
Description
When scientific software is written to specify processes, it takes the form of a workflow, and is often written in an ad-hoc manner in a dynamic programming language. There is a proliferation of legacy workflows implemented by non-expert programmers due to the accessibility of dynamic languages. Unfortunately, ad-hoc workflows lack a structured description as provided by specialized management systems, making ad-hoc workflow maintenance and reuse difficult, and motivating the need for analysis methods. The analysis of ad-hoc workflows using compiler techniques does not address dynamic languages - a program has so few constrains that its behavior cannot be predicted. In contrast, workflow provenance tracking has had success using run-time techniques to record data. The aim of this work is to develop a new analysis method for extracting workflow structure at run-time, thus avoiding issues with dynamics.
The method captures the dataflow of an ad-hoc workflow through its execution and abstracts it with a process for simplifying repetition. An instrumentation system first processes the workflow to produce an instrumented version, capable of logging events, which is then executed on an input to produce a trace. The trace undergoes dataflow construction to produce a provenance graph. The dataflow is examined for equivalent regions, which are collected into a single unit. The workflow is thus characterized in terms of its treatment of an input. Unlike other methods, a run-time approach characterizes the workflow's actual behavior; including elements which static analysis cannot predict (for example, code dynamically evaluated based on input parameters). This also enables the characterization of dataflow through external tools.
The contributions of this work are: a run-time method for recording a provenance graph from an ad-hoc Python workflow, and a method to analyze the structure of a workflow from provenance. Methods are implemented in Python and are demonstrated on real world Python workflows. These contributions enable users to derive graph structure from workflows. Empowered by a graphical view, users can better understand a legacy workflow. This makes the wealth of legacy ad-hoc workflows accessible, enabling workflow reuse instead of investing time and resources into creating a workflow.
The method captures the dataflow of an ad-hoc workflow through its execution and abstracts it with a process for simplifying repetition. An instrumentation system first processes the workflow to produce an instrumented version, capable of logging events, which is then executed on an input to produce a trace. The trace undergoes dataflow construction to produce a provenance graph. The dataflow is examined for equivalent regions, which are collected into a single unit. The workflow is thus characterized in terms of its treatment of an input. Unlike other methods, a run-time approach characterizes the workflow's actual behavior; including elements which static analysis cannot predict (for example, code dynamically evaluated based on input parameters). This also enables the characterization of dataflow through external tools.
The contributions of this work are: a run-time method for recording a provenance graph from an ad-hoc Python workflow, and a method to analyze the structure of a workflow from provenance. Methods are implemented in Python and are demonstrated on real world Python workflows. These contributions enable users to derive graph structure from workflows. Empowered by a graphical view, users can better understand a legacy workflow. This makes the wealth of legacy ad-hoc workflows accessible, enabling workflow reuse instead of investing time and resources into creating a workflow.
ContributorsAcűna, Ruben (Author) / Bazzi, Rida (Thesis advisor) / Lacroix, Zoé (Thesis advisor) / Candan, Kasim (Committee member) / Arizona State University (Publisher)
Created2015