Learning and Activity Patterns in OSS Communities and their Impact on Software Quality

This paper presents a framework to identify and analyse learning and activity patterns that characterise participation and collaboration of individuals in Open Source Software (OSS) communities.  It first describes how participants’ activities enable and drive a learning process that occurs in individual participants as well as in the OSS project community as a whole. It then explores how to identify and analyse learning patterns at both individual level and community level. The objective of such analysis is to determine the impact of these patterns on the quality of the OSS product and define a descriptive approach to quality that is concerned less with standards than with the facts of OSS peer-review and peer-production

Using antipatterns to improve the quality of FLOSS development

Antipatterns have been mostly reported in closed source software environments. With the advent of Free/Libre Open Source Software (FLOSS), researchers have started analysing popular FLOSS projects, seeking vitality indicators and success patterns.  However, an impressively high percentage of FLOSS projects are unsuccessful.  Moreover, even in the successful cases of FLOSS there can be found tracks of failed attempts, dead-ends, forks, abandonments etc.  FLOSS antipatterns can help developers to improve their code and improve the communication and collaboration within the FLOSS community.  In this paper, we present some example of FLOSS antipatterns and discuss the benefits that they bring to various FLOSS user roles.  Furthermore, we present ontology-based technology and software tools that can be used to assist FLOSS developers and community users to identify, document, share antipatterns and use these mechanisms to assist FLOSS projects conform to specified requirements.  Finally, we propose a framework for the quantitative identification of the antipatterns to use as quality indicators in the certification of FLOSS products

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

Automating Source Code Refactoring in the Classroom

Refactoring is the practice of improving software quality without altering its external behavior. Developers intuitively refactor their code for multiple purposes, such as improving program comprehension, reducing code complexity, dealing with technical debt, and removing code smells. However, no prior studies have exposed the students to an experience of the process of antipatterns detection and refactoring correction, and provided students with toolset to practice it. To understand and increase the awareness of refactoring concepts, in this paper, we aim to reflect on our experience with teaching refactoring and how it helps students become more aware of bad programming practices and the importance of correcting them via refactoring. This paper discusses the results of an experiment in the classroom that involved carrying out various refactoring activities for the purpose of removing antipatterns using JDeodorant, an Eclipse plugin that supports antipatterns detection and refactoring. The results of the quantitative and qualitative analysis with 171 students show that students tend to appreciate the idea of learning refactoring and are satisfied with various aspects of the JDeodorant plugin's operation. Through this experiment, refactoring can turn into a vital part of the computing educational plan. We envision our findings enabling educators to support students with refactoring tools tuned towards safer and trustworthy refactoring

Towards Semantic Detection of Smells in Cloud Infrastructure Code

Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies.Comment: 5 pages, 6 figures. The 10 th International Conference on Web Intelligence, Mining and Semantics (WIMS 2020

Critiquing Antipatterns In Novice Code

Students in introductory computer science courses, are learning to program. Indeed, most students perceive that learning to code is the central topic explored in the courses. Students spend an enormous amount of time struggling to learn the syntax and understand semantics of a particular language. Instructors spend a similar amount of time reading student code and explaining the meaning of the cryptic error messages displayed by compilers. Messages provided by compilers are intended to give feedback on the adherence of one’s code to the language specification and conventions. Unfortunately, these message are geared towards experts who have a clear understanding of the language syntax and semantics and a deep model of what comprises a program and how a program is developed. These students are novices who lack fundamental understanding of the structure of a program and have no basic mental model of how a program works. Novices make different kinds of mistakes than experts. Instructors need to spend a lot of time simply assisting novices in using compilers and understanding their output. In addition to mastering the syntax and semantics of their first programming language, novices are exposed to the question of what constitutes good design. Instructors can identify virtuous design choices and articulate areas of improvement. But contact time with students is limited, and waiting for in-person feedback or replies to personal messages can be a critical delay. Novices, still struggling to use the compiler, have not yet developed the sophisticated analytical processes employed by experts and this is reflected in their design choices and the kinds of mistakes they make. When a novice approaches an instructor with a question, the instructor must often provide a balanced critique that assists the student with understanding both the structure and the design aspects of their own code. My research has focused on whether we can identify examples of early programming antipatterns that have arisen from our teaching experience, and describe different ways of detecting them automatically. Novice students may produce code that is close to a correct solution but contains syntactic errors; code critiquers attempt to salvage the promising portions of the students submission and suggest repairs in ways more meaningful than typical compiler error messages. Alternatively, a student misunderstanding may result in well-formed code that passes unit tests yet contains clear design flaws; through additional analysis, code critiquers can detect and flag these flaws. Finally, certain types of antipatterns can be anticipated and flagged by the instructor, based on the context of the course and the programming activity; code critiquers allow for customizable critique triggers and messages. This dissertation presents several key contributions to our understanding of novice misconceptions and their representation, diagnosis and repair using antipatterns. My research focuses on identifying antipatterns and detecting them in novice code, then using this information to provide the student with a meaningful critique of their work. I have developed WebTA, a tool to critique student programs in introductory computer science courses. WebTA is used to teach students test-driven agile development methods through small cycles of teaching, coding integrated with testing, and immediate feedback.Through the use of WebTA in introductory computer science courses since 2014, I have amassed a significant corpus of novice programmer submission data. Lastly, I have compiled a library of antipatterns found in novice code

Pattern languages in HCI: A critical review

This article presents a critical review of patterns and pattern languages in human-computer interaction (HCI). In recent years, patterns and pattern languages have received considerable attention in HCI for their potential as a means for developing and communicating information and knowledge to support good design. This review examines the background to patterns and pattern languages in HCI, and seeks to locate pattern languages in relation to other approaches to interaction design. The review explores four key issues: What is a pattern? What is a pattern language? How are patterns and pattern languages used? and How are values reflected in the pattern-based approach to design? Following on from the review, a future research agenda is proposed for patterns and pattern languages in HCI