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Efficient Algorithms for Finite Automata
Polanský, Ondřej ; Lengál, Ondřej (referee) ; Holík, Lukáš (advisor)
This thesis compares languages C++, C#, OCaml and Python based on speed, memory requirements and programming comfort. The goal of this thesis is to find out how much does the choice of a certain programming language impact the performance of programs working with finite automata. The same set of basic and advanced automata algorithms was implemented in each language and their efficiency was measured on a sample of 200 finite automata using a unix based operating system. Finally, the author presents results and discusses suitability of each language for work with finite automata. This thesis can help with selecting an appropriate programming language for multitude of purposes, including development of automata algorithm libraries or the process of designing programs and prototypes that work with finite automata.
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Static Analysis Using Facebook Infer Focused on Performance Analysis
Pavela, Ondřej ; Lengál, Ondřej (referee) ; Rogalewicz, Adam (advisor)
Static analysis has nowadays become one of the most popular ways of catching bugs early in the modern software. However, reasonably precise static analysis tools still often struggle to scale well on large and quickly changing codebases. Efficient static analysers, such as Coverity or Code Sonar, are usually proprietary and difficult to openly evaluate or extend. On the contrary, Facebook Infer offers an open source static analysis framework with the emphasis on compositional, incremental and consequently highly scalable inter-procedural analysis. This thesis presents Looper --- a new performance oriented resource bounds analyser which extends the capabilities of Facebook Infer. We have based our implementation on an existing resource bounds analyser Loopus and evaluated it on two different test suites, showing encouraging results in comparison with the existing Cost analyser developed by the Infer team.
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Efficient Algorithms for Finite Automata
Polanský, Ondřej ; Lengál, Ondřej (referee) ; Holík, Lukáš (advisor)
This thesis compares languages C++, C#, OCaml and Python based on speed, memory requirements and programming comfort. The goal of this thesis is to find out how much does the choice of a certain programming language impact the performance of programs working with finite automata. The same set of basic and advanced automata algorithms was implemented in each language and their efficiency was measured on a sample of 200 finite automata using a unix based operating system. Finally, the author presents results and discusses suitability of each language for work with finite automata. This thesis can help with selecting an appropriate programming language for multitude of purposes, including development of automata algorithm libraries or the process of designing programs and prototypes that work with finite automata.
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Static Analysis Using Facebook Infer Focused on Performance Analysis
Pavela, Ondřej ; Lengál, Ondřej (referee) ; Rogalewicz, Adam (advisor)
Static analysis has nowadays become one of the most popular ways of catching bugs early in the modern software. However, reasonably precise static analysis tools still often struggle to scale well on large and quickly changing codebases. Efficient static analysers, such as Coverity or Code Sonar, are usually proprietary and difficult to openly evaluate or extend. On the contrary, Facebook Infer offers an open source static analysis framework with the emphasis on compositional, incremental and consequently highly scalable inter-procedural analysis. This thesis presents Looper --- a new performance oriented resource bounds analyser which extends the capabilities of Facebook Infer. We have based our implementation on an existing resource bounds analyser Loopus and evaluated it on two different test suites, showing encouraging results in comparison with the existing Cost analyser developed by the Infer team.
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