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论文范文
1. Introduction Large sets of programs are important in a number of areas of computer science and engineering. For example, in supervised machine learning (ML) for performance autotuning, a sufficiently large number of training programs are needed to represent the desired program space. Similarly, in compiler testing, successfully running test programs through a compiler increases confidence in its functionality and correctness. Finally, in software testing, the adequacy of the testing strategy of a program is measured by testing a large number of faulty mutant versions of the program [1]. The percentage of mutants for which errors are detected is used as a measure of the adequacy of the testing. However, the number of real programs available for use is often limited. For compilers, it can be difficult to build up a diverse set of programs that contain enough functionality combinations and error scenarios. Similarly, benchmark suites used to evaluate performance of software and systems [2, 3] usually consist of only tens of programs and are usually too small to build sufficiently large and diverse training sets for ML models. Finally, a large number of mutant programs are needed to increase confidence in a testing strategy. Thus, program generators are often used to produce synthetic programs for use in such situations. There are several existing program generators [4–6]. However, these generators suffer from limitations, in particular, the lack of user control over the generated code [4], inflexible and restrictive use cases or target languages [6–8], and difficulties with associated tools [6]. Ad hoc methods of generating large program sets, such as the use of Perl or Python scripts, also have their own limitations; the resulting scripts are difficult to write, maintain, and extend. Thus, in this work, we design, implement, and evaluate Genesis, a program generation language that addresses the above shortcomings. Genesis facilitates the generation of synthetic programs in a statistically controlled fashion. It allows users to annotate a template program to identify and parameterize those segments of the program they wish to vary, the values each parameter may take, and the desired statistical distribution of these values across generated programs. The Genesis preprocessor uses the annotations to generate programs based on a template program, with the values of each parameter drawn from its corresponding distribution. Genesis is unique in that it allows the generation of synthetic code with controlled statistical properties, which is important in some application domains. The constructs of the language provide a simple yet flexible means of varying template code. They also allow for the hierarchical composition of generated code segments. This facilitates the generation of large numbers of programs that are arbitrarily long with only a handful of constructs. It also makes it easy to create, modify, and extend existing Genesis programs. Genesis is target-language agnostic in that it can be used with template programs written in various programming languages. 
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