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Per pastaruosius metus, lankstaus programavimo metodikos sulaukė daug dėmesio. Programų kodo pertvarkymas tapo viena iš labiausiai naudojamų praktikų, ypač ekstremaliame programavime. Todėl poreikis turėti programos kodo pertvarkymų įrankį išaugo iki tokio lygio, kad pertvarkymo įrankiai tapo reikalaujama priemone šiuolaikinėse programų kūrimo aplinkose. Tyrimo sritis – abstrakčios sintaksės medžių pertvarkymo algoritmai. Tyrimo objektas – abstrakčios sintaksės medžių pertvarkymo procesai skirtingose programavimo kalbose (C++ ir Java). Šio darbo tikslas – ištirti, kaip efektyviai būtų galima vykdyti pertvarkymus bendriniam AST, tam sukuriant pertvarkymų biblioteką ir atliekant eksperimentus įvairių kalbų atvejais. / Over the past years, agile development methodologies have attracted a lot of attention. Refactoring has become one of the most heavily used practices, especially in Extreme Programming. Therefore the need to have powerful refactoring tools has grown to such an extent, that there is a required feature for modern-day IDEs to have implemented refactoring tools. The aim of this work is to build a refactoring library for the generic abstract syntax tree and test the algorithms for speed. Generic abstract syntax tree (GAST) is a tree structure that can store elements of several programming language. Achieving this goal required doing certain tasks that you find in this document: analysis of technologies and existing software; several algorithms for refactoring; user need and specification of requirements; library model, expressed in UML diagrams; test plan and test procedure. Experiments have shown that it is necessary to improve the refactoring algorithms. But the library and the AST structure is worth further development.

Operatorless Prolog text is LL(1) in nature and any standard LL parser generator tool can be used to parse it. However, the Prolog text that conforms to the ISO Prolog standard allows the definition of dynamic operators. Since Prolog operators can be defined at run-time, operator symbols are not present in the grammar rules of the language. Unless the parser generator allows for some flexibility in the specification of the grammar rules, it is very difficult to generate a parser for such text. In this thesis we discuss the existing parsing methods and their modified versions to parse languages with dynamic operator capabilities. Implementation details of a parser using Javacc as a parser generator tool to parse standard Prolog text is provided. The output of the parser is an “Abstract Syntax Tree” that reflects the correct precedence and associativity rules among the various operators (static and dynamic) of the language. Empirical results are provided that show that a Prolog parser that is generated by the parser generator like Javacc is comparable in efficiency to a hand-coded parser.

This thesis describes the Drag language. Drag is a general purpose, gradually typed, lexically scoped, and multi-paradigm pro- gramming language. The essence of Drag is to build the abstract syntax trees of the programs directly and interactively. Our work includes the language specification and a prototype program. The language specification focuses on the syntax, the semantic model, and the type system. The prototype consists of an interactive editor and a compiler that targets several plat- forms, among which we focus on the LLVM platform in this thesis.

<p>An unavoidable amount of money is spent on maintaining existing software systems today. Software maintenance cost generally higher than development cost of the system therefore lowering maintenance cost is highly appreciated in software industry.</p><p>A significant part of maintenance activities is related to repeating the investigation of problems and applying repeated solutions several times. A software system may contain a common bug in several different places and it might take extra effort and time to fix all existences of this bug. This operation commonly increases the cost of Software Maintenance Activities.</p><p>Detecting duplicate code fragments can significantly decrease the time and effort therefore the maintenance cost. Clone code detection can be achieved via analyzing the source code of given software system. An abstract syntax tree based clone detector for java systems is designed and implemented through this study.</p><p>This master thesis examines a software engineering process to create an abstract syntax tree based clone detector for the projects implemented in Java programming language.</p>

An unavoidable amount of money is spent on maintaining existing software systems today. Software maintenance cost generally higher than development cost of the system therefore lowering maintenance cost is highly appreciated in software industry. A significant part of maintenance activities is related to repeating the investigation of problems and applying repeated solutions several times. A software system may contain a common bug in several different places and it might take extra effort and time to fix all existences of this bug. This operation commonly increases the cost of Software Maintenance Activities. Detecting duplicate code fragments can significantly decrease the time and effort therefore the maintenance cost. Clone code detection can be achieved via analyzing the source code of given software system. An abstract syntax tree based clone detector for java systems is designed and implemented through this study. This master thesis examines a software engineering process to create an abstract syntax tree based clone detector for the projects implemented in Java programming language.

Today, universities rely heavily on systems for detecting plagiarism in students’essays and reports. Code submissions however require specific tools. A numberof approaches to finding plagiarisms in code have already been tried, includingtechniques based on comparing textual transformations of code, token strings,parse trees and graph representations. In this master’s thesis, a new system, cojac,is presented which combines textual, tree and graph techniques to detect a broadspectrum of plagiarism attempts. The system finds plagiarisms in C, C++ and Adasource files. This thesis discusses the method used for obtaining parse trees fromthe source code and the abstract syntax tree analysis. For comparison of syntaxtrees, we generate sets of fingerprints, digest forms of trees, which makes thecomparison algorithm more scalable. To evaluate the method, a set of benchmarkfiles have been constructed containing plagiarism scenarios which was analyzedboth by our system and Moss, another available system for plagiarism detection incode. The results show that our abstract syntax tree analysis can effectively detectplagiarisms such as changing the format of the code and renaming of identifiersand is at least as effective as Moss for detecting plagiarisms of these kinds

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Trusted execution environments (TEEs) are an emerging technology that provides a protected hardware environment for processing and storing sensitive information. By using TEEs, developers can bolster the security of software systems. However, incorporating TEE into existing software systems can be a costly and labor-intensive endeavor. Software maintenance—changing software after its initial release—is known to contribute the majority of the cost in the software development lifecycle. The first step of making use of a TEE requires that developers accurately identify which pieces of code would benefit from being protected in a TEE. For large code bases, this identification process can be quite tedious and time-consuming. To help reduce the software maintenance costs associated with introducing a TEE into existing software, this thesis introduces ML-TEE, a recommendation tool that uses a deep learning model to classify whether an input function handles sensitive information or sensitive code. By applying ML-TEE, developers can reduce the burden of manual code inspection and analysis. ML-TEE's model was trained and tested on functions from GitHub repositories that use Intel SGX and on an imbalanced dataset. The accuracy of the final model used in the recommendation system has an accuracy of 98.86% and an F1 score of 80.00%. In addition, we conducted a pilot study, in which participants were asked to identify functions that needed to be placed inside a TEE in a third-party project. The study found that on average, participants who had access to the recommendation system's output had a 4% higher accuracy and completed the task 21% faster. / Master of Science / Improving the security of software systems has become critically important. A trusted execution environment (TEE) is an emerging technology that can help secure software that uses or stores confidential information. To make use of this technology, developers need to identify which pieces of code handle confidential information and should thus be placed in a TEE. However, this process is costly and laborious because it requires the developers to understand the code well enough to make the appropriate changes in order to incorporate a TEE. This process can become challenging for large software that contains millions of lines of code. To help reduce the cost incurred in the process of identifying which pieces of code should be placed within a TEE, this thesis presents ML-TEE, a recommendation system that uses a deep learning model to help reduce the number of lines of code a developer needs to inspect. Our results show that the recommendation system achieves high accuracy as well as a good balance between precision and recall. In addition, we conducted a pilot study and found that participants from the intervention group who used the output from the recommendation system managed to achieve a higher average accuracy and perform the assigned task faster than the participants in the control group.