Model-based source code refactoring with interaction and visual cues

Refactoring source code involves the developer in a myriad of program detail that can obscure the design changes that they actually wish to bring about. On the other hand, refactoring a UML model of the code makes it easier to focus on the program design, but the burdensome task of applying the refactorings to the source code is left to the developer. In an attempt to obtain the advantages of both approaches, we propose a refactoring approach where the interaction with the developer takes place at the model level, but the actual refactoring occurs on the source code itself. We call this approach model-based source code refactoring and implement it in this paper using two tools: (1) Design-Imp enables the developer to use interactive search-based design exploration to create a UML-based desired design from an initial design extracted from the source code. It also provides visual cues to improve developer comprehension during the design-level refactoring process and to help the developer to discern between promising and poor refactoring solutions. (2) Code-Imp then refactors the original source so that it has the same functional behavior as the original program, and a design close to the one produced in the design exploration phase, that is, a design that has been confirmed as “desirable” by the developer. We evaluated our approach involving interaction and visual cues with industrial developers refactoring three Java projects, comparing it with an approach using interaction without visual cues and a fully automated approach. The results show that our approach yields refactoring sequences that are more acceptable both to the individual developer and to a set of independent expert refactoring evaluators. Furthermore, our approach removed more code smells and was evaluated very positively by the experiment participants.</p

A Systematic Mapping Study of Empirical Studies Performed with Collections of Software Projects

Contexto: los proyectos software son insumos comunes en los experimentos de la Ingeniería del Software, aunque estos muchas veces sean seleccionados sin seguir una estrategia específica, lo cual disminuye la representatividad y replicación de los resultados. Una opción es el uso de colecciones preservadas de proyectos software, pero estas deben ser vigentes y con reglas explícitas que aseguren su actualización a lo largo del tiempo. Objetivo: realizar un estudio secundario sistematizado sobre las estrategias de selección de los proyectos software en estudios empíricos para conocer las reglas tenidas en cuenta, el grado de uso de colecciones de proyectos, los metadatos extraídos y los análisis estadísticos posteriores realizados. Método: se utilizó un mapeo sistemático para identificar estudios publicados desde enero de 2013 a diciembre de 2020. Resultados: se identificaron 122 estudios de los cuales el 72% utilizó reglas propias para la selección de proyectos y un 27% usó colecciones de proyectos existentes. Asimismo, no se encontraron evidencias de un marco estandarizado para la selección de proyectos, ni la aplicación de métodos estadísticos que se relacionen con la estrategia de recolección de las muestras.Context: software projects are commonresources in Software Engineering experiments,although these are often selected without following a specific strategy, which reduces the representativeness and replication of the results. An option is the use of preserved collections of software projects, but these must be current, with explicit guidelines that guarante etheir updating over a long period of time. Goal: to carry out a systematic secondary study about the strategies to select software projects in empirical studies to discover the guidelines taken into account, the degree of use of project collections, the meta-data extracted and the subsequent statistical analysis conducted. Method: A systematic mapping study to identify studies published from January 2013 to December 2020. Results: 122 studies were identified, of which the 72% used their own guidelines for project selection and the 27% used existent project collections. Likewise, there was no evidence of a standardized framework for the project selection process, nor the application of statistical methods that relates with the sample collection strategy.Fil: Carruthers, Juan Andrés. Universidad Nacional del Nordeste. Facultad de Cs.exactas Naturales y Agrimensura. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Irrazábal, Emanuel Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Cs.exactas Naturales y Agrimensura. Departamento de Informatica; Argentin

A Systematic Mapping Study of Empirical Studies Performed with Collections of Software Projects

Contexto: los proyectos software son insumos comunes en los experimentos de la Ingeniería del Software, aunque estos muchas veces sean seleccionados sin seguir una estrategia específica, lo cual disminuye la representatividad y replicación de los resultados. Una opción es el uso de colecciones preservadas de proyectos software, pero estas deben ser vigentes y con reglas explícitas que aseguren su actualización a lo largo del tiempo. Objetivo: realizar un estudio secundario sistematizado sobre las estrategias de selección de los proyectos software en estudios empíricos para conocer las reglas tenidas en cuenta, el grado de uso de colecciones de proyectos, los metadatos extraídos y los análisis estadísticos posteriores realizados. Método: se utilizó un mapeo sistemático para identificar estudios publicados desde enero de 2013 a diciembre de 2020. Resultados: se identificaron 122 estudios de los cuales el 72% utilizó reglas propias para la selección de proyectos y un 27% usó colecciones de proyectos existentes. Asimismo, no se encontraron evidencias de un marco estandarizado para la selección de proyectos, ni la aplicación de métodos estadísticos que se relacionen con la estrategia de recolección de las muestras.Context: software projects are commonresources in Software Engineering experiments,although these are often selected without following a specific strategy, which reduces the representativeness and replication of the results. An option is the use of preserved collections of software projects, but these must be current, with explicit guidelines that guarante etheir updating over a long period of time. Goal: to carry out a systematic secondary study about the strategies to select software projects in empirical studies to discover the guidelines taken into account, the degree of use of project collections, the meta-data extracted and the subsequent statistical analysis conducted. Method: A systematic mapping study to identify studies published from January 2013 to December 2020. Results: 122 studies were identified, of which the 72% used their own guidelines for project selection and the 27% used existent project collections. Likewise, there was no evidence of a standardized framework for the project selection process, nor the application of statistical methods that relates with the sample collection strategy.Fil: Carruthers, Juan Andrés. Universidad Nacional del Nordeste. Facultad de Cs.exactas Naturales y Agrimensura. Departamento de Informatica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; ArgentinaFil: Diaz Pace, Jorge Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Irrazábal, Emanuel Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste; Argentina. Universidad Nacional del Nordeste. Facultad de Cs.exactas Naturales y Agrimensura. Departamento de Informatica; Argentin

Explainable, Security-Aware and Dependency-Aware Framework for Intelligent Software Refactoring

As software systems continue to grow in size and complexity, their maintenance continues to become more challenging and costly. Even for the most technologically sophisticated and competent organizations, building and maintaining high-performing software applications with high-quality-code is an extremely challenging and expensive endeavor. Software Refactoring is widely recognized as the key component for maintaining high-quality software by restructuring existing code and reducing technical debt. However, refactoring is difficult to achieve and often neglected due to several limitations in the existing refactoring techniques that reduce their effectiveness. These limitation include, but not limited to, detecting refactoring opportunities, recommending specific refactoring activities, and explaining the recommended changes. Existing techniques are mainly focused on the use of quality metrics such as coupling, cohesion, and the Quality Metrics for Object Oriented Design (QMOOD). However, there are many other factors identified in this work to assist and facilitate different maintenance activities for developers: 1. To structure the refactoring field and existing research results, this dissertation provides the most scalable and comprehensive systematic literature review analyzing the results of 3183 research papers on refactoring covering the last three decades. Based on this survey, we created a taxonomy to classify the existing research, identified research trends and highlighted gaps in the literature for further research. 2. To draw attention to what should be the current refactoring research focus from the developers’ perspective, we carried out the first large scale refactoring study on the most popular online Q&A forum for developers, Stack Overflow. We collected and analyzed posts to identify what developers ask about refactoring, the challenges that practitioners face when refactoring software systems, and what should be the current refactoring research focus from the developers’ perspective. 3. To improve the detection of refactoring opportunities in terms of quality and security in the context of mobile apps, we designed a framework that recommends the files to be refactored based on user reviews. We also considered the detection of refactoring opportunities in the context of web services. We proposed a machine learning-based approach that helps service providers and subscribers predict the quality of service with the least costs. Furthermore, to help developers make an accurate assessment of the quality of their software systems and decide if the code should be refactored, we propose a clustering-based approach to automatically identify the preferred benchmark to use for the quality assessment of a project. 4. Regarding the refactoring generation process, we proposed different techniques to enhance the change operators and seeding mechanism by using the history of applied refactorings and incorporating refactoring dependencies in order to improve the quality of the refactoring solutions. We also introduced the security aspect when generating refactoring recommendations, by investigating the possible impact of improving different quality attributes on a set of security metrics and finding the best trade-off between them. In another approach, we recommend refactorings to prioritize fixing quality issues in security-critical files, improve quality attributes and remove code smells. All the above contributions were validated at the large scale on thousands of open source and industry projects in collaboration with industry partners and the open source community. The contributions of this dissertation are integrated in a cloud-based refactoring framework which is currently used by practitioners.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/171082/1/Chaima Abid Final Dissertation.pdfDescription of Chaima Abid Final Dissertation.pdf : Dissertatio