The effectiveness of refactoring, based on a compatibility testing taxonomy and a dependency graph

In this paper, we describe and then appraise a testing taxonomy proposed by van Deursen and Moonen (VD&M) based on the post-refactoring repeatability of tests. Four categories of refactoring are identified by VD&M ranging from semantic-preserving to incompatible, where, for the former, no new tests are required and for the latter, a completely new test set has to be developed. In our appraisal of the taxonomy, we heavily stress the need for the inter-dependence of the refactoring categories to be considered when making refactoring decisions and we base that need on a refactoring dependency graph developed as part of the research. We demonstrate that while incompatible refactorings may be harmful and time-consuming from a testing perspective, semantic-preserving refactorings can have equally unpleasant hidden ramifications despite their advantages. In fact, refactorings which fall into neither category have the most interesting properties. We support our results with empirical refactoring data drawn from seven Java open-source systems (OSS) and from the same analysis form a tentative categorization of code smells

A meta-analysis approach to refactoring and XP

The mechanics of seventy-two different Java refactorings are described fully in Fowler's text. In the same text, Fowler describes seven categories of refactoring, into which each of the seventy-two refactorings can be placed. A current research problem in the refactoring and XP community is assessing the likely time and testing effort for each refactoring, since any single refactoring may use any number of other refactorings as part of its mechanics and, in turn, can be used by many other refactorings. In this paper, we draw on a dependency analysis carried out as part of our research in which we identify the 'Use' and 'Used By' relationships of refactorings in all seven categories. We offer reasons why refactorings in the 'Dealing with Generalisation' category seem to embrace two distinct refactoring sub-categories and how refactorings in the 'Moving Features between Objects' category also exhibit specific characteristics. In a wider sense, our meta-analysis provides a developer with concrete guidelines on which refactorings, due to their explicit dependencies, will prove problematic from an effort and testing perspective

Mutation Testing as a Safety Net for Test Code Refactoring

Refactoring is an activity that improves the internal structure of the code without altering its external behavior. When performed on the production code, the tests can be used to verify that the external behavior of the production code is preserved. However, when the refactoring is performed on test code, there is no safety net that assures that the external behavior of the test code is preserved. In this paper, we propose to adopt mutation testing as a means to verify if the behavior of the test code is preserved after refactoring. Moreover, we also show how this approach can be used to identify the part of the test code which is improperly refactored

Detection of microservice smells through static analysis

A arquitetura de microsserviços é um modelo arquitetural promissor na área de software, atraindo desenvolvedores e empresas para os seus princípios convincentes. As suas vantagens residem no potencial para melhorar a escalabilidade, a flexibilidade e a agilidade, alinhando se com as exigências em constante evolução da era digital. No entanto, navegar entre as complexidades dos microsserviços pode ser uma tarefa desafiante, especialmente à medida que este campo continua a evoluir. Um dos principais desafios advém da complexidade inerente aos microsserviços, em que o seu grande número e interdependências podem introduzir novas camadas de complexidade. Além disso, a rápida expansão dos microsserviços, juntamente com a necessidade de aproveitar as suas vantagens de forma eficaz, exige uma compreensão mais profunda das potenciais ameaças e problemas que podem surgir. Para tirar verdadeiramente partido das vantagens dos microsserviços, é essencial enfrentar estes desafios e garantir que o desenvolvimento e a adoção de microsserviços sejam bem-sucedidos. O presente documento pretende explorar a área dos smells da arquitetura de microsserviços que desempenham um papel tão importante na dívida técnica dirigida à área dos microsserviços. Embarca numa exploração de investigação abrangente, explorando o domínio dos smells de microsserviços. Esta investigação serve como base para melhorar um catálogo de smells de microsserviços. Esta investigação abrangente obtém dados de duas fontes primárias: systematic mapping study e um questionário a profissionais da área. Este último envolveu 31 profissionais experientes com uma experiência substancial no domínio dos microsserviços. Além disso, são descritos o desenvolvimento e o aperfeiçoamento de uma ferramenta especificamente concebida para identificar e resolver problemas relacionados com os microsserviços. Esta ferramenta destina-se a melhorar o desempenho dos programadores durante o desenvolvimento e a implementação da arquitetura de microsserviços. Por último, o documento inclui uma avaliação do desempenho da ferramenta. Trata-se de uma análise comparativa efetuada antes e depois das melhorias introduzidas na ferramenta. A eficácia da ferramenta será avaliada utilizando o mesmo benchmarking de microsserviços utilizado anteriormente, para além de outro benchmarking para garantir uma avaliação abrangente.The microservices architecture stands as a beacon of promise in the software landscape, drawing developers and companies towards its compelling principles. Its appeal lies in the potential for improved scalability, flexibility, and agility, aligning with the ever-evolving demands of the digital age. However, navigating the intricacies of microservices can be a challenging task, especially as this field continues to evolve. A key challenge arises from the inherent complexity of microservices, where their sheer number and interdependencies can introduce new layers of intricacy. Furthermore, the rapid expansion of microservices, coupled with the need to harness their advantages effectively, demands a deeper understanding of the potential pitfalls and issues that may emerge. To truly unlock the benefits of microservices, it is essential to address these challenges head-on and ensure a successful journey in the world of microservices development and adoption. The present document intends to explore the area of microservice architecture smells that play such an important role in the technical debt directed to the area of microservices. It embarks on a comprehensive research exploration, delving into the realm of microservice smells. This research serves as the cornerstone for enhancing a microservice smell catalogue. This comprehensive research draws data from two primary sources: a systematic mapping research and an industry survey. The latter involves 31 seasoned professionals with substantial experience in the field of microservices. Moreover, the development and enhancement of a tool specifically designed to identify and address issues related to microservices is described. This tool is aimed at improving developers' performance throughout the development and implementation of microservices architecture. Finally, the document includes an evaluation of the tool's performance. This involves a comparative analysis conducted before and after the tool's enhancements. The tool's effectiveness will be assessed using the same microservice benchmarking as previously employed, in addition to another benchmark to ensure a comprehensive evaluation

State of Refactoring Adoption: Towards Better Understanding Developer Perception of Refactoring

Context: Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. Objective: We aim at exploring how developers document their refactoring activities during the software life cycle. We call such activity Self-Affirmed Refactoring (SAR), which is an indication of the developer-related refactoring events in the commit messages. After that, we propose an approach to identify whether a commit describes developer-related refactoring events, to classify them according to the refactoring common quality improvement categories. To complement this goal, we aim to reveal insights into how reviewers develop a decision about accepting or rejecting a submitted refactoring request, what makes such review challenging, and how to the efficiency of refactoring code review. Method: Our empirically driven study follows a mixture of qualitative and quantitative methods. We text mine refactoring-related documentation, then we develop a refactoring taxonomy, and automatically classify a large set of commits containing refactoring activities, and identify, among the various quality models presented in the literature, the ones that are more in-line with the developer\u27s vision of quality optimization, when they explicitly mention that they are refactoring to improve them to obtain an enhanced understanding of the motivation behind refactoring. After that, we performed an industrial case study with professional developers at Xerox to study the motivations, documentation practices, challenges, verification, and implications of refactoring activities during code review. Result: We introduced SAR taxonomy on how developers document their refactoring strategies in commit messages and proposed a SAR model to automate the detection of refactoring. Our survey with code reviewers has revealed several difficulties related to understanding the refactoring intent and implications on the functional and non-functional aspects of the software. Conclusion: Our SAR taxonomy and model, can work in conjunction with refactoring detectors, to report any early inconsistency between refactoring types and their documentation and can serve as a solid background for various empirical investigations. In light of our findings of the industrial case study, we recommended a procedure to properly document refactoring activities, as part of our survey feedback

MiSFIT: Mining Software Fault Information and Types

As software becomes more important to society, the number, age, and complexity of systems grow. Software organizations require continuous process improvement to maintain the reliability, security, and quality of these software systems. Software organizations can utilize data from manual fault classification to meet their process improvement needs, but organizations lack the expertise or resources to implement them correctly. This dissertation addresses the need for the automation of software fault classification. Validation results show that automated fault classification, as implemented in the MiSFIT tool, can group faults of similar nature. The resulting classifications result in good agreement for common software faults with no manual effort. To evaluate the method and tool, I develop and apply an extended change taxonomy to classify the source code changes that repaired software faults from an open source project. MiSFIT clusters the faults based on the changes. I manually inspect a random sample of faults from each cluster to validate the results. The automatically classified faults are used to analyze the evolution of a software application over seven major releases. The contributions of this dissertation are an extended change taxonomy for software fault analysis, a method to cluster faults by the syntax of the repair, empirical evidence that fault distribution varies according to the purpose of the module, and the identification of project-specific trends from the analysis of the changes

How we refactor and how we document it? On the use of supervised machine learning algorithms to classify refactoring documentation

Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. To cope with the above-mentioned limitations, we aim to better understand what motivates developers to apply a refactoring by mining and automatically classifying a large set of 111,884 commits containing refactoring activities, extracted from 800 open source Java projects. We trained a multi-class classifier to categorize these commits into three categories, namely, Internal Quality Attribute, External Quality Attribute, and Code Smell Resolution, along with the traditional Bug Fix and Functional categories. This classification challenges the original definition of refactoring, being exclusive to improving software design and fixing code smells. Furthermore, to better understand our classification results, we qualitatively analyzed commit messages to extract textual patterns that developers regularly use to describe their refactoring activities. The results of our empirical investigation show that (1) fixing code smells is not the main driver for developers to refactoring their code bases. Refactoring is solicited for a wide variety of reasons, going beyond its traditional definition; (2) the distribution of refactoring operations differs between production and test files; (3) developers use a variety of patterns to purposefully target refactoring-related activities; (4) the textual patterns, extracted from commit messages, provide better coverage for how developers document their refactorings

Rise of the Planet of Serverless Computing: A Systematic Review

Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications. It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164 papers on 17 research directions of serverless computing, including performance optimization, programming framework, application migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms for serverless computing, as well as promising research opportunities