Code smells detection and visualization: A systematic literature review

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been cataloged with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Method: Over 83 primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning techniques are used in 35% of the studies. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches, several methods are used, such as city metaphors, 3D visualization techniques. Conclusions: We confirm that the detection of CS is a non-trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of CS; increasing the diversity of detected CS and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of CS detection and visualization techniques validation experiments.info:eu-repo/semantics/acceptedVersio

Code smells detection and visualization: A systematic literature review

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Method: Over 83 primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning techniques are used in 35% of the studies. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches several methods are used, such as: city metaphors, 3D visualization techniques. Conclusions: We confirm that the detection of CS is a non trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of CS; increasing the diversity of detected CS and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of CS detection and visualization techniques validation experiments.Comment: submitted to ARC

An Analytical Study of Code Smells

Software development process involves developing, building and enhancing high-quality software for specific tasks and as a consequence generates considerable amount of data. This data can be managed in a systematic manner creating knowledge repositories that can be used to competitive advantage. Lesson\u27s learned as part of the development process can also be part of the knowledge bank and can be used to advantage in subsequent projects by developers and software practitioners. Code smells are a group of symptoms which reveal that code is not good enough and requires some actions to have a cleansed code. Software metrics help to detect code smells while refactoring methods are used for removing them. Furthermore, various tools are applicable for detecting of code smells. A Code smell repository organizes all the available knowledge in the literature about code smells and related concepts. An analytical study of code smells is presented in this paper which extracts useful, actionable and indicative knowledge

Streamlining code smells: Using collective intelligence and visualization

Context. Code smells are seen as major source of technical debt and, as such, should be detected and removed. Code smells have long been catalogued with corresponding mitigating solutions called refactoring operations. However, while the latter are supported in current IDEs (e.g., Eclipse), code smells detection scaffolding has still many limitations. Researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigate the problem of smells-infected code. Objective. This thesis presents a new approach to code smells detection that we have called CrowdSmelling and the results of a validation experiment for this approach. The latter is based on supervised machine learning techniques, where the wisdom of the crowd (of software developers) is used to collectively calibrate code smells detection algorithms, thereby lessening the subjectivity issue. Method. In the context of three consecutive years of a Software Engineering course, a total “crowd” of around a hundred teams, with an average of three members each, classified the presence of 3 code smells (Long Method, God Class, and Feature Envy) in Java source code. These classifications were the basis of the oracles used for training six machine learning algorithms. Over one hundred models were generated and evaluated to determine which machine learning algorithms had the best performance in detecting each of the aforementioned code smells. Results. Good performances were obtained for God Class detection (ROC=0.896 for Naive Bayes) and Long Method detection (ROC=0.870 for AdaBoostM1), but much lower for Feature Envy (ROC=0.570 for Random Forrest). Conclusions. Obtained results suggest that Crowdsmelling is a feasible approach for the detection of code smells, but further validation experiments are required to cover more code smells and to increase external validityContexto. Os cheiros de código são a principal causa de dívida técnica (technical debt), como tal, devem ser detectados e removidos. Os cheiros de código já foram há muito tempo catalogados juntamente com as correspondentes soluções mitigadoras chamadas operações de refabricação (refactoring). No entanto, embora estas últimas sejam suportadas nas IDEs actuais (por exemplo, Eclipse), a deteção de cheiros de código têm ainda muitas limitações. Os investigadores argumentam que a subjectividade do processo de deteção de cheiros de código é um dos principais obstáculo à mitigação do problema da qualidade do código. Objectivo. Esta tese apresenta uma nova abordagem à detecção de cheiros de código, a que chamámos CrowdSmelling, e os resultados de uma experiência de validação para esta abordagem. A nossa abordagem de CrowdSmelling baseia-se em técnicas de aprendizagem automática supervisionada, onde a sabedoria da multidão (dos programadores de software) é utilizada para calibrar colectivamente algoritmos de detecção de cheiros de código, diminuindo assim a questão da subjectividade. Método. Em três anos consecutivos, no âmbito da Unidade Curricular de Engenharia de Software, uma "multidão", num total de cerca de uma centena de equipas, com uma média de três membros cada, classificou a presença de 3 cheiros de código (Long Method, God Class, and Feature Envy) em código fonte Java. Estas classificações foram a base dos oráculos utilizados para o treino de seis algoritmos de aprendizagem automática. Mais de cem modelos foram gerados e avaliados para determinar quais os algoritmos de aprendizagem de máquinas com melhor desempenho na detecção de cada um dos cheiros de código acima mencionados. Resultados. Foram obtidos bons desempenhos na detecção do God Class (ROC=0,896 para Naive Bayes) e na detecção do Long Method (ROC=0,870 para AdaBoostM1), mas muito mais baixos para Feature Envy (ROC=0,570 para Random Forrest). Conclusões. Os resultados obtidos sugerem que o Crowdsmelling é uma abordagem viável para a detecção de cheiros de código, mas são necessárias mais experiências de validação para cobrir mais cheiros de código e para aumentar a validade externa

Assessing architectural evolution: A case study

This is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2011 SpringerThis paper proposes to use a historical perspective on generic laws, principles, and guidelines, like Lehman’s software evolution laws and Martin’s design principles, in order to achieve a multi-faceted process and structural assessment of a system’s architectural evolution. We present a simple structural model with associated historical metrics and visualizations that could form part of an architect’s dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand, to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach

Multi-Sensory Interaction for Blind and Visually Impaired People

This book conveyed the visual elements of artwork to the visually impaired through various sensory elements to open a new perspective for appreciating visual artwork. In addition, the technique of expressing a color code by integrating patterns, temperatures, scents, music, and vibrations was explored, and future research topics were presented. A holistic experience using multi-sensory interaction acquired by people with visual impairment was provided to convey the meaning and contents of the work through rich multi-sensory appreciation. A method that allows people with visual impairments to engage in artwork using a variety of senses, including touch, temperature, tactile pattern, and sound, helps them to appreciate artwork at a deeper level than can be achieved with hearing or touch alone. The development of such art appreciation aids for the visually impaired will ultimately improve their cultural enjoyment and strengthen their access to culture and the arts. The development of this new concept aids ultimately expands opportunities for the non-visually impaired as well as the visually impaired to enjoy works of art and breaks down the boundaries between the disabled and the non-disabled in the field of culture and arts through continuous efforts to enhance accessibility. In addition, the developed multi-sensory expression and delivery tool can be used as an educational tool to increase product and artwork accessibility and usability through multi-modal interaction. Training the multi-sensory experiences introduced in this book may lead to more vivid visual imageries or seeing with the mind’s eye

Reactive Microservices - An Experiment

Os microserviços são geralmente adotados quando a escalabilidade e flexibilidade de uma aplicação são essenciais para o seu sucesso. Apesar disto, as dependências entre serviços transmitidos através de protocolos síncronos, resultam numa única falha que pode afetar múltiplos microserviços. A adoção da capacidade de resposta numa arquitetura baseada em microserviços, através da reatividade, pode facilitar e minimizar a proliferação de erros entre serviços e na comunicação entre eles, ao dar prioridade à capacidade de resposta e à resiliência de um serviço. Esta dissertação fornece uma visão geral do estado da arte dos microserviços reativos, estruturada através de um processo de mapeamento sistemático, onde são analisados os seus atributos de qualidade mais importantes, os seus erros mais comuns, as métricas mais adequadas para a sua avaliação, e as frameworks mais relevantes. Com a informação recolhida, é apresentado o valor deste trabalho, onde a decisão do projeto e a framework a utilizar são tomadas, através da técnica de preferência de ordem por semelhança com a solução ideal e o processo de hierarquia analítica, respetivamente. Em seguida, é realizada a análise e o desenho da solução, para o respetivo projeto, onde se destacam as alterações arquiteturais necessárias para o converter num projeto de microserviços reativo. Em seguida, descreve-se a implementação da solução, começando pela configuração do projeto necessária para agilizar o processo de desenvolvimento, seguida dos principais detalhes de implementação utilizados para assegurar a reatividade e como a framework apoia e simplifica a sua implementação, finalizada pela configuração das ferramentas de métricas no projeto para apoiar os testes e a avaliação da solução. Em seguida, a validação da solução é investigada e executada com base na abordagem Goals, Questions, Metrics (GQM), para estruturar a sua análise relativamente à manutenção, escalabilidade, desempenho, testabilidade, disponibilidade, monitorabilidade e segurança, finalizada pela conclusão do trabalho global realizado, onde são listadas as contribuições, ameaças à validade e possíveis trabalhos futuros.Microservices are generally adopted when the scalability and flexibility of an application are essential to its success. Despite this, dependencies between services transmitted through synchronous protocols result in one failure, potentially affecting multiple microservices. The adoption of responsiveness in a microservices-based architecture, through reactivity, can facilitate and minimize the proliferation of errors between services and in the communication between them by prioritizing the responsiveness and resilience of a service. This dissertation provides an overview of the reactive microservices state of the art, structured through a systematic mapping process, where its most important quality attributes, pitfalls, metrics, and most relevant frameworks are analysed. With the gathered information, the value of this work is presented, where the project and framework decision are made through the technique of order preference by similarity to the ideal solution and the analytic hierarchy process, respectively. Then, the analysis and design of the solution are idealized for the respective project, where the necessary architectural changes are highlighted to convert it to a reactive microservices project. Next, the solution implementation is described, starting with the necessary project setup to speed up the development process, followed by the key implementation details employed to ensure reactivity and how the framework streamlines its implementation, finalized by the metrics tools setup in the project to support the testing and evaluation of the solution. Then, the solution validation is traced and executed based on the Goals, Questions, Metrics (GQM) approach to structure its analysis regarding maintainability, scalability, performance, testability, availability, monitorability, and security, finalized by the conclusion of the overall work done, where the contributions, threats to validity and possible future work are listed

Assessing the effect of source code characteristics on changeability

Maintenance is the phase of the software lifecycle that comprises any modification after the delivery of an application. Modifications during this phase include correcting faults, improving internal attributes, as well as adapting the application to different environments. As application knowledge and architectural integrity degrade over time, so does the facility with which changes to the application are introduced. Thus, eliminating source code that presents characteristics that hamper maintenance becomes necessary if the application is to evolve. We group these characteristics under the term Source Code Issues. Even though there is support for detecting Source Code Issues, the extent of their harmfulness for maintenance remains unknown. One of the most studied Source Code Issue is cloning. Clones are duplicated code, usually created as programmers copy, paste, and customize existing source code. However, there is no agreement on the harmfulness of clones. This thesis proposes and follows a novel methodology to assess the effect of clones on the changeability of methods. Changeability is the ease with which a source code entity is modified. It is assessed through metrics calculated from the history of changes of the methods. The impact of clones on the changeability of methods is measured by comparing the metrics of methods that contain clones to those that do not. Source code characteristics are then tested to establish whether they are endemic of methods whose changeability decay increase when cloned. In addition to findings on the harmfulness of cloning, this thesis contributes a methodology that can be applied to assess the harmfulness of other Source Code Issues. The contributions of this thesis are twofold. First, the findings answer the question about the harmfulness of clones on changeability by showing that cloned methods are more likely to change, and that some cloned methods have significantly higher changeability decay when cloned. Furthermore, it offers a characterization of such harmful clones. Second, the methodology provides a guide to analyze the effect of Source Code Characteristics in changeability; and therefore, can be adapted for other Source Code Issues

Unlocking the Pragmatics of Emoji: Evaluation of the Integration of Pragmatic Markers for Sarcasm Detection

Emojis have become an integral element of online communications, serving as a powerful, under-utilised resource for enhancing pragmatic understanding in NLP. Previous works have highlighted their potential for improvement of more complex tasks such as the identification of figurative literary devices including sarcasm due to their role in conveying tone within text. However present state-of-the-art does not include the consideration of emoji or adequately address sarcastic markers such as sentiment incongruence. This work aims to integrate these concepts to generate more robust solutions for sarcasm detection leveraging enhanced pragmatic features from both emoji and text tokens. This was achieved by establishing methodologies for sentiment feature extraction from emojis and a depth statistical evaluation of the features which characterise sarcastic text on Twitter. Current convention for generation of training data which implements weak-labelling using hashtags or keywords was evaluated against a human-annotated baseline; postulated validity concerns were verified where statistical evaluation found the content features deviated significantly from the baseline, highlighting potential validity concerns for many prominent works on the topic to date. Organic labelled sarcastic tweets containing emojis were crowd sourced by means of a survey to ensure valid outcomes for the sarcasm detection model. Given an established importance of both semantic and sentiment information, a novel sentiment-aware attention mechanism was constructed to enhance pattern recognition, balancing core features of sarcastic text: sentiment incongruence and context. This work establishes a framework for emoji feature extraction; a key roadblock cited in literature for their use in NLP tasks. The proposed sarcasm detection pipeline successfully facilitates the task using a GRU neural network with sentiment-aware attention, at an accuracy of 73% and promising indications regarding model robustness as part of a framework which is easily scalable for the inclusion of any future emojis released. Both enhanced sentiment information to supplement context in addition to consideration of the emoji were found to improve outcomes for the task

Assessing architectural evolution: a case study

This paper proposes to use a historical perspective on generic laws, principles, and guidelines, like Lehman’s software evolution laws and Martin’s design principles, in order to achieve a multi-faceted process and structural assessment of a system’s architectural evolution. We present a simple structural model with associated historical metrics and visualizations that could form part of an architect’s dashboard. We perform such an assessment for the Eclipse SDK, as a case study of a large, complex, and long-lived system for which sustained effective architectural evolution is paramount. The twofold aim of checking generic principles on a well-know system is, on the one hand, to see whether there are certain lessons that could be learned for best practice of architectural evolution, and on the other hand to get more insights about the applicability of such principles. We find that while the Eclipse SDK does follow several of the laws and principles, there are some deviations, and we discuss areas of architectural improvement and limitations of the assessment approach