From a Domain Analysis to the Specification and Detection of Code and Design Smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences\ud on development and maintenance. Consequently, several smell detection\ud approaches and tools have been proposed in the literature. However,\ud so far, they allow the detection of predefined smells but the detection\ud of new smells or smells adapted to the context of the analysed systems\ud is possible only by implementing new detection algorithms manually.\ud Moreover, previous approaches do not explain the transition from\ud specifications of smells to their detection. Finally, the validation\ud of the existing approaches and tools has been limited on few proprietary\ud systems and on a reduced number of smells. In this paper, we introduce\ud an approach to automate the generation of detection algorithms from\ud specifications written using a domain-specific language. This language\ud is defined from a thorough domain analysis. It allows the specification\ud of smells using high-level domain-related abstractions. It allows\ud the adaptation of the specifications of smells to the context of\ud the analysed systems.We specify 10 smells, generate automatically\ud their detection algorithms using templates, and validate the algorithms\ud in terms of precision and recall on Xerces v2.7.0 and GanttProject\ud v1.10.2, two open-source object-oriented systems.We also compare\ud the detection results with those of a previous approach, iPlasma

An automatic approach to detecting and eliminating lazy classes based on abstract syntax trees

Abstract To detect and eliminate lazy classes in source code, an automatic approach based on abstract syntax trees (ASTs) is proposed. Source code files transform to ASTs at first, then the relationships between classes are extracted from the ASTs. Three common relationships are considered, which are generalization, association and dependency. Some definitions are proposed to represent the classes set of different kinds of relationships. After carrying out several set operations on these sets, the candidate lazy classes set is obtained. By further manual examination, the true lazy classes are acquired. Finally, a specific lazy class will be removed automatically from the project. Four projects are tested to detect and eliminate the lazy classes. The experimental results show that the proposed detection algorithm has high precision rate. In addition, this approach has good efficiency, and its execution time has a linear relationship to the size of a system

Study of Code Smells: A Review and Research Agenda

Code Smells have been detected, predicted and studied by researchers from several perspectives. This literature review is conducted to understand tools and algorithms used to detect and analyze code smells to summarize research agenda. 114 studies have been selected from 2009 to 2022 to conduct this review. The studies are deeply analyzed under the categorization of machine learning and non-machine learning, which are found to be 25 and 89 respectively. The studies are analyzed to gain insight into algorithms, tools and limitations of the techniques. Long Method, Feature Envy, and Duplicate Code are reported to be the most popular smells. 38% of the studies focused their research on the enhancement of tools and methods. Random Forest and JRip algorithms are found to give the best results under machine learning techniques. We extended the previous studies on code smell detection tools, reporting a total 87 tools during the review. Java is found to be the dominant programming language during the study of smells

Defining linguistic antipatterns towards the improvement of source code quality

Previous studies showed that linguistic aspect of source code is a valuable source of information that can help to improve program comprehension. The proposed research work focuses on supporting quality improvement of source code by identifying, specifying, and studying common negative practices (i.e., linguistic antipatterns) with respect to linguistic information. We expect the definition of linguistic antipatterns to increase the awareness of the existence of such bad practices and to discourage their use. We also propose to study the relation between negative practices in linguistic information (i.e., linguistic antipatterns) and negative practices in structural information (i.e., design antipatterns) with respect to comprehension effort and fault/change proneness. We discuss the proposed methodology and some preliminary results

Um metamodelo para apoiar a formalização de convenções de codificação

Orientador: Leonardo MontecchiDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: As convenções de codificação são um meio de melhorar a confiabilidade dos sistemas de software. Elas podem ser estabelecidas por vários motivos, desde melhorar a legibilidade do código até evitar a introdução de falhas de segurança. No entanto, convenções de codificação geralmente vêm na forma de documentos textuais em linguagem natural, o que as torna difíceis de gerenciar e aplicar. Seguindo os princípios de engenharia orientados a modelos, nesta dissertação, propomos uma abordagem e uma linguagem para especificar convenções de codificação usando modelos estruturados. Chamamos tal linguagem de Coding Conventions Specification Language (CCSL). Também propomos uma transformação de modelo para gerar automaticamente verificadores a partir de uma especificação CCSL para encontrar violações das regras especificadas. Para avaliar a proposta, realizamos dois experimentos. O primeiro experimento tem como objetivo avaliar o metamodelo CCSL, enquanto o outro tem como objetivo verificar a capacidade dos verificadores de encontrar violações da regra especificada nos códigos Java. Os resultados obtidos são promissores e sugerem que a abordagem proposta é viável. No entanto, eles também destacam que muitos desafios ainda precisam ser superados. No primeiro experimento, analisamos um total de 216 regras individuais de dois grandes conjuntos de convenções de codificação existentes. No geral, foi possível representar 63% das regras de codificação consideradas usando nossa linguagem. No segundo experimento, selecionamos 53 regras dentre as implementadas na ferramenta PMD (um analisador de código popular) para comparar os resultados entre nossa ferramenta e a ferramenta PMD em três projetos reais. Em geral, alcançamos resultados iguais ou melhores da ferramenta PMD em mais da metade das regras selecionadas (79%), enquanto apenas 6% das regras não puderam ser especificadas usando nossa linguagem. Nas regras restantes, os resultados apresentados foram diferentes para cada uma das ferramentas. Concluímos discutindo instruções para trabalhos futurosAbstract: Coding conventions are a means to improve the reliability of software systems. They can be established for many reasons, ranging from improving the readability of code to avoiding the introduction of security flaws. However, coding conventions often come in the form of textual documents in natural language, which makes them hard to be managed and to enforced. Following model-driven engineering principles, in this dissertation we propose an approach and language for specifying coding conventions using structured models. We call this language Coding Conventions Specification Language (CCSL). We also propose a model transformation to concretely generate checkers to find violations of the rules specified with our language. To evaluate the proposal, we performed two experiments. The first experiment aims to evaluate the Coding Conventions Specification Language metamodel, while the other aims to check the capability of the derived checkers to find violations of the specified rule in Java codes. The obtained results are promising and suggest that the proposed approach is feasible. However, they also highlight that many challenges still need to be overcome. In the first experiment, we analyzed a total of 216 individual rules from two large sets of existing coding conventions. Overall, it was possible to represent 63% of the considered coding rules using our language. In the second experiment, we selected 53 rules from those implemented in the PMD tool to compare the results between our tool and the PMD tool in three real projects. In general, we achieve equal or better results of the PMD tool in more than half of the selected rules (79%), while only 6% of the rules could not be specified using our language. There are also cases where PMD performed better than our approach (9%) as well as cases where the results were different for each of the tools (6%). We conclude by discussing directions for future worksMestradoCiência da ComputaçãoMestre em Ciência da Computação2018/11129-8FAPES

Efficient Extension of a Software Analysis Framework to Additional Languages

In the current era of software development, multi-language codebases are common, and change propagation in these codebases is challenging. The existing change propagation tool ModCP is a solution that can assist software developers with propagating changes across several languages, but only one at a time. However, ModCP has some architectural problems in that make supporting new languages hard to develop and maintain for a long time. In addition, supporting change propagation across code snippets consisting of a programming language embedded inside a different programming language would be a useful feature for ModCP. To achieve this, we must detect the embedded code snippets in a code being analyzed by ModCP. In this thesis, we develop a new, more efficient architecture for ModCP, involving a single abstract model that each language extends for its usage, resulting in complete isolation between language results. We compare our approach with a baseline version that uses the same, concrete model for all languages and adds new models when necessary. Our approach reduces code complexity and development time and makes code more compatible with best practices of development compared to the baseline. Moreover, we design a system for ModCP to guess and validate the programming language used in code snippets, based on the initial detection of keywords, as input to execute change propagation for multi-language codes embedded inside each other. We compare our keyword detection approach with existing deep learning and brute force approaches and show that our method is the best choice if accuracy, performance, and scalability are needed simultaneously