Education of programming language is often included in Czech secondary education as a method of implementation of corresponding school standards. By the time of publishing this thesis, these standards are only vaguely defined, so the effect of teaching programming can't be objectively evaluated from the perspective of the standards. This thesis elaborates on the usability of educational programming languages with respect to cognitive level of secondary school students, which may be used as a founded source in defining standards of particular schools.

Doctorat en Sciences / info:eu-repo/semantics/nonPublished

The goal of this thesis is to explore the current possibilities of implementation of compilers of low-level functional languages. It is expected to evaluate theoretical possibilities of functional programming languages, possible limitations that arise from the absence of run-time code support in low-level environment, and to implement a language compiler that demonstrates some chosen properties. Powered by TCPDF (www.tcpdf.org)

Viper is a verification infrastructure developed at ETH Zurich. Using this infrastructure, programs written in the Viper language may be analyzed for correctness with respect to assertions and contracts. In this thesis, we develop a contracts library and a translator program that compiles C# code into the Viper language and thus allows it to be verified. A user may annotate their C# program with these contracts and then use the translator to determine its functional correctness. The translator supports most C# features, including types and arrays. It also integrates with Visual Studio, showing translation and verification errors to the user on-the-fly.

Considerable work has been recently devoted to the automatic generation of syntax analyzers. This work has been generally concerned with extending the power of the parser generator technigues, rather than improving the syntactic specification mechanism. Se present a new parsing language which attempts to unify syntax and semantics. In addition, the language provides a mechanism by which reasonable error recovery can be naturally included in the syntax specification. / Science, Faculty of / Computer Science, Department of / Graduate

Code decomposition (also known as functional decomposition) is the process of breaking a larger problem into smaller subproblems so that each function implements only a single task. Although code decomposition is integral to computer science, it is often overlooked in introductory computer science education due to the challenges of teaching it given limited resources. Earthworm is a tool that generates unique suggestions on how to improve the decomposition of provided Python source code. Given a program as input, Earthworm presents the user with a list of suggestions to improve the functional decomposition of the program. Each suggestion includes the lines of code that can be refactored into a new function, the arguments that must be passed to this function and the variables returned from the function. The tool is intended to be used in introductory computer science courses to help students learn more about decomposition. Earthworm generates suggestions by converting Python source code into a control flow graph. Static analysis is performed on the control flow graph to direct the generation of suggestions based on code slices.

Programming languages like Python, JavaScript, and Ruby are becoming increasingly popular due to their dynamic capabilities. These languages are often much easier to learn than other, statically type checked, languages such as C++ or Rust. Unfortunately, these dynamic languages come at the cost of losing compile-time optimizations. Python is arguably the most popular language for data scientists and researchers in the artificial intelligence and machine learning communities. As this research becomes increasingly popular, and the problems these researchers face become increasingly computationally expensive, questions are being raised about the performance of languages like Python. Language features found in Python, more specifically dynamic typing and run-time modification of object attributes, preclude common static analysis optimizations that often yield improved performance. This thesis attempts to quantify the cost of dynamic features in Python. Namely, the run-time modification of objects and scope as well as the dynamic type system. We introduce Cannoli, a Python 3.6.5 compiler that enforces restrictions on the language to enable opportunities for optimization. The Python code is compiled into an intermediate representation, Rust, which is further compiled and optimized by the Rust pipeline. We show that the analyzed features cause a significant reduction in performance and we quantify the cost of these features for language designers to consider.

Testing code is important, but writing test cases can be time consuming, particularly for beginning programmers who are already struggling to write an implementation. We present TestBuilder, a system for test case generation which uses an SMT solver to generate inputs to reach specified lines in a function, and asks the user what the expected outputs would be for those inputs. The resulting test cases check the correctness of the output, rather than merely ensuring the code does not crash. Further, by querying the user for expectations, TestBuilder encourages the programmer to think about what their code ought to do, rather than assuming that whatever it does is correct. We demonstrate, using mutation testing of student projects, that tests generated by TestBuilder perform better than merely compiling the code using Python’s built-in compile function, although they underperform the tests students write when required to achieve 100% test coverage.

R is a programming language and a tool used mostly in statistics and data analysis domains, with a rich package-based extension system. GNU-R, the standard interpreter of R, in version 3.5.0 introduced a new native API (ALTREP) for R extensions developers. The goal of the thesis is to implement this API for FastR, an interpreter of R based on GraalVM and Truffle, and explore options for optimization of FastR in context of this API. The motivation is to increase the number of extensions that can be installed and run on FastR. 1

<div>Elasticity is an essential feature for cloud applications to handle varying and unpredictable workloads in a cost-effective way on cloud platforms. However, implementing a stateful elastic application is hard, as programmers have to: (1) reason about concurrent execution in the applications (serializability); (2) guarantee the application can process more requests with larger scale (scalability); and (3) provide elasticity management to improve performance and resource efficiency for applications (efficient elasticity management). Unfortunately, addressing all those concerns requires deep understanding and rich experience in distributed systems and cloud computing. </div><div> </div><div>In this dissertation, we provide programming support to help programmers implement their stateful elastic cloud applications in a simpler manner. Specifically, we present AEON, an actor-based programming language, and \arch, an elastic programming framework. On the one hand, AEON provides programmers with scalability and serialzability, executing actor-based programs in a serialized manner while still retaining a high degree of parallelism. Meanwhile, AEON can adjust programs' scale via fine-grained live actor migration. On the other hand, PLASMA includes (1) an elastic programming language as a second ``level'' of programming (complementing the main application programming language) for describing elasticity behaviors, and (2) a novel semantics-aware elasticity management runtime that tracks program execution and acts upon application features as suggested by elasticity behaviors. </div><div>With these, PLASMA can provide efficient elasticity management to cloud applications</div>