Title: Refactoring tree editor TrEd Author: Peter Fabian Department: Institute of Formal and Applied Linguistics Supervisor: doc. Ing. Zdenek Zabokrtsky, Ph.D., Institute of Formal and Applied Linguistics Abstract: The main goal of the thesis was to refactor tree editor TrEd, improve its modularity, maintainability and make its further development less difficult. Static and dynamic analysis of TrEd have been performed in order to help us find acrid spots in the source code. More than 230 subroutines and data structures have been moved between packages, 50 new packages and a test suite with more than 1,300 tests have been created. A new coding style have been chosen for further development and most severe violations of this standard have been fixed. After the changes done on the source code, it have been analyzed again and the results have been compared with the previous state. Keywords: Tree Editor TrEd, Perl, code refactoring, code analysis

Thesis (Master, Computing) -- Queen's University, 2007-09-25 12:38:48.455 / Software maintenance is the lifecycle activity that consumes the greatest amount of resources. Maintenance is a difficult task because of the size of software systems. Much of the time spent on maintenance is spent trying to understand source code. Refactoring offers a way to improve source code design and quality. We present an approach to refactoring that is based on the functionality of source code. Sets of heuristics are captured as patterns of source code. Refactoring opportunities are located using these patterns, and dependencies are verified to check if the located refactorings preserve the dependencies in the source code. Our automated tool performs the functional-based refactoring opportunities detection process, verifies dependencies, and performs the refactorings that preserve dependencies. These refactorings transform the source code into a series of functional regions of code, which makes it easier for developers to locate code they are searching for. This also creates a chunked structure in the source code, which helps with bottom-up program comprehension. Thus, this process reduces the amount of time required for maintenance by reducing the amount of time spent on program comprehension. We perform case studies to demonstrate the effectiveness of our automated approach on two open source applications. / Master

Code modularization provides benefits throughout the software life cycle; however, the presence of crosscutting concerns (CCCs) in software hinders its complete modularization. This thesis describes IVCon, a GUI-based tool that provides a novel approach to modularization of CCCs. IVCon enables users to create, examine, and modify their code in two different views, the woven view and the unwoven view. The woven view displays program code in colors that indicate which CCCs various code segments implement. The unwoven view displays code in two panels, one showing the core of the program and the other showing all the code implementing each concern in an isolated module. IVCon aims to provide an easy-to-use interface for conveniently creating, examining, and modifying code in, and translating between, the woven and unwoven views.

Identifiers play a key role in code quality and comprehension, as poorly named identifiers hinder the developers’ ability to understand, debug, and maintain programs. To address these issues, several studies have proposed methods to automatically rename low-quality or inconsistent identifiers. Recently, machine learning has been used to predict potential renaming opportunities for identifiers. However, there is none or little work done that reviews the key machine learning-based methods used to rename identifiers. To that end, this project aims to conduct a systematic literature review that will answer: a) what key machine learning-based approaches exist for recommeding renamings of identifiers; b) how accurate are the different approaches; and c) what datasets are used for their evaluation. As a result of the literature review, we selected 14 learning-based identifier renaming approaches published between 2014-2021. From the extracted data, we identified a total of 19 machine learning techniques, which we categorized into a taxonomy of "deep" and "shallow" learning. In this process, we found that a majority of studies since 2019 have used deep learning techniques. Specifically, two context-based approaches achieved the best performance in detecting and renaming inconsistent identifiers. As a result, we discussed how the use of different techniques might have influenced the performance, evaluation methods, and research practices in the area of rename refactoring.

Software refactoring is a crucial approach in both development and maintenance to improve the efficiency, maintainability, and structure of software systems. However, a number of challenges remain in the way of the effective implementation of software refactoring techniques within Sri Lanka's software development industry. This thesis investigates the challenger in software refactoring process in Sri Lanka software development companies and examine the intentions of developers, software test automation engineer and project managers on the usage on the machine learning techniques for software refactoring and the study uses the Unified Theory of Acceptance and usage of Technology 2 (UTAUT2) extended model. The study demonstrates that professional in software development Industry have positive intentions toward the usage of machine learning techniques, motivated by benefits they perceive, such as increased productivity, maintenance, and improved code quality. This study advances our understanding of software refactoring and theadoption of new ML technologies and offers insightful information to researchers, practitioners, and decision- makers in the Sri Lankan IT sector and beyond.

This study investigates the potential of Large Language Models (LLMs) to automate and enhance software maintenance tasks, focusing on bug detection and code refactoring. Traditional software maintenance, which includes debugging and code optimization, is time-consuming and prone to human error. With advancements in artificial intelligence, LLMs like ChatGPT and Copilot offer promising capabilities for automating these tasks. Through a series of quasi-experiments, we evaluate the effectiveness of ChatGPT 3.5, ChatGPT 4 (Grimoire GPT), and GitHub Copilot. Each model was tested on various code snippets to measure their ability to identify and correct bugs and refactor code while maintaining its original functionality. The results indicatethat ChatGPT 4 (Grimoire GPT) outperforms the other models, demonstrating superior accuracy and effectiveness, with success percentages of 87.5% and 75% in bug detection and code refactoring respectively. This research highlights the potential of advanced LLMs to significantly reduce the time and cost associated with software maintenance, though human oversight is still necessary to ensure code integrity. The findings contribute to the understanding of LLM capabilities in real-world software engineering tasks and pave the way for more intelligent and efficient software maintenance practices.

Every second, there are many attempts to attack various entities on the Internet. This is why high-quality, fast, and up-to-date tools are needed to easily analyze network traffic. Netfox Detective is one of such tools. Specifically, it is used for forensic analysis of network communication. The aim of this work is to migrate Netfox Detective to the newest version of .NET platform (.NET 5), including refactoring with respect to user experience and correct use of software design patterns. This thesis deals not only with the migration itself, but is listing common mistakes programmers make along with possible solutions to these mistakes. The chapters contain a detailed decision log that can help guide other developers to better solutions. Furthermore, the work deals with analysis and creation of unit tests and with correct use of tools for CI/CD. Fully migrated project is not the only output of this thesis. A development environment for the project has been prepared in GitLab and it is ready to be used.

Denna rapport har sitt ursprung i det kodefaktoriseringsarbete som utfärdats våren 2013 som examensarbete i dataingenjörsprogrammet vid Örebro Universitet. Arbetet utfärdades på Nethouse i Örebro, och hade stort fokus på koddesign och kodkvalitet. I rapporten diskuteras vilka faktorer som påverkar hur underhållbar och läsbar en kod är, men också hur man på ett rimligt sätt kan utvärdera och mäta kodkvalitet. Den teoretiska biten blandas med den praktiska, där läsaren introduceras för ett flertal metoder, och hur dessa sedan implementerades i det faktiska projektet som Nethouse tillhandahöll. / This report has its origins in the code refactoring work issued in spring 2013 as a Degree Project in the Computer Engineering Programme, at Örebro University. The work took place at Nethouse in Örebro, and had a major focus on code design, and code quality. The report discusses the factors that affect how maintainable and readable a code is, but also how to reasonably evaluate and measure code quality. The theory is mixed with the practical, where the reader is introduced to a variety of methods, and how these were then implemented in the actual project that Nethouse provided.