Design Smells Research Articles

An Empirical Study on the Evolution of Design Smells

The evolution of software systems often leads to its architectural degradation due to the presence of design problems. In the literature, design smells have been defined as indicators of such problems. In particular, the presence of design smells could indicate the use of constructs that are harmful to system maintenance activities. In this work, an investigation on the nature and presence of design smells has been performed. An empirical study has been conducted considering the complete history of eight software systems, commit by commit. The detection of instances of multiple design smell types has been performed at each commit, and the analysis of the relationships between the detected smells and the maintenance activities, specifically due to refactoring activities, has been investigated. The proposed study evidenced that classes affected by design smells are more subject to change, especially when multiple smells are detected in the same classes. Moreover, it emerged that in some cases these smells are removed, and this occurs involving more smells at the same time. Finally, results indicate that smells removals are not correlated to the refactoring activities.

Exploring the Connection between Design Smells and Security Vulnerabilities

Software quality aims at having quality as part of all aspects of the developed software. Design smells are considered enemies of the software source code quality. There are verities of design problems with different terminologies. Researchers and practitioners accept it as true that whenever there is a design smell, there is a security issue or concern. In this work, we want to explore the connection between design smells and security vulnerabilities. This work provides experimental evidence about this connection. We conducted an empirical study to explore the connection between design smells and security issues by evaluating four C# open-source systems. We found interesting results that show classes with design smells have more chances of having security issues.

Integrated Formal Tools for Software Architecture Smell Detection

The architecture smells are the poor design practices applied to the software architecture design. The smells in software architecture design can be cascaded to cause the issues in the system implementation and significantly affect the maintainability and reliability attribute of the software system. The prevention of architecture smells at the design phase can therefore improve the overall quality of the software system. This paper presents a framework that supports the detection of architecture smells based on the formalization of architecture design. Our modeling specification supports representing both structural and behavioral aspect of software architecture design; it allows the smells to be analyzed and detected with the provided tools. Our framework has been applied to seven architecture smells that violate different design principles. The evaluation has been conducted and the result shows that our detection approach gives accurate results and performs well on different size of models. With the proposed framework, other architecture smells can be defined and detected using the process and tools presented in this paper.

O CHEIRO NO PROJETO DE INTERIORES COMERCIAIS

Este artigo apresenta uma reflexão sobre as possibilidades de incorporação dos cheiros no projeto de interiores, mais especificamente, nos ambientes comerciais. Partindo-se de um referencial teórico multidisciplinar, abarcando o ergodesign e o design emocional, busca-se evidenciar a importância de se contemplar o sentido do olfato nos projetos do ambiente construído.

Software Design Smell Detection: a systematic mapping study

Design Smells are indicators of situations that negatively affect software quality attributes such as understandability, testability, extensibility, reusability, and maintainability in general. Improving maintainability is one of the cornerstones of making software evolution easier. Hence, Design Smell Detection is important in helping developers when making decisions that can improve software evolution processes. After a long period of research, it is important to organize the knowledge produced so far and to identify current challenges and future trends. In this paper, we analyze 18 years of research into Design Smell Detection. There is a wide variety of terms that have been used in the literature to describe concepts which are similar to what we have defined as “Design Smells,” such as design defect, design flaw, anomaly, pitfall, antipattern, and disharmony. The aim of this paper is to analyze all these terms and include them in the study. We have used the standard systematic literature review method based on a comprehensive set of 395 articles published in different proceedings, journals, and book chapters. We present the results in different dimensions of Design Smell Detection, such as the type or scope of smell, detection approaches, tools, applied techniques, validation evidence, type of artifact in which the smell is detected, resources used in evaluation, supported languages, and relation between detected smells and software quality attributes according to a quality model. The main contributions of this paper are, on the one hand, the application of domain modeling techniques to obtain a conceptual model that allows the organization of the knowledge on Design Smell Detection and a collaborative web application built on that knowledge and, on the other, finding how tendencies have moved across different kinds of smell detection, as well as different approaches and techniques. Key findings for future trends include the fact that all automatic detection tools described in the literature identify Design Smells as a binary decision (having the smell or not), which is an opportunity to evolve to fuzzy and prioritized decisions. We also find that there is a lack of human experts and benchmark validation processes, as well as demonstrating that Design Smell Detection positively influences quality attributes.

User interface design smell: Automatic detection and refactoring of Blob listeners

Context. User Interfaces (UIs) intensively rely on event-driven programming: interactive objects send UI events, which capture users’ interactions, to dedicated objects called controllers. Controllers use several UI listeners that handle these events to produce UI commands.Objective. First, we reveal the presence of design smells in the code that describes and controls UIs. Second, we demonstrate that specific code analyses are necessary to analyze and refactor UI code, because of its coupling with the rest of the code.Method. We conducted an empirical study on four large Java software systems. We studied to what extent the number of UI commands that a UI listener can produce has an impact on the change- and fault-proneness of the UI listener code. We developed a static code analysis for detecting UI commands in the code.Results. We identified a new type of design smell, called Blob listener, that characterizes UI listeners that can produce more than two UI commands. We proposed a systematic static code analysis procedure that searches for Blob listener that we implement in InspectorGuidget. We conducted experiments on the four software systems for which we manually identified 53 instances of Blob listener. InspectorGuidget successfully detected 52 Blob listeners out of 53. The results exhibit a precision of 81.25% and a recall of 98.11%. We then developed a semi-automatically and behavior-preserving refactoring process to remove Blob listeners. 49.06% of the 53 Blob listeners were automatically refactored. Patches have been accepted and merged. Discussions with developers of the four software systems assess the relevance of the Blob listener.Conclusion. This work shows that UI code also suffers from design smells that have to be identified and characterized. We argue that studies have to be conducted to find other UI design smells and tools that analyze UI code must be developed.

Analyzing software evolution and quality by extracting Asynchrony change patterns

Abstract Change patterns describe two or more files were often changed together during the development or the maintenance of software systems. Several studies have been presented to detect change patterns and to analyze their types and their impact on software quality. In this context, we introduced the Asynchrony change pattern to describes a set of files that always change together in the same change periods, regardless developers who maintained them. In this paper, we investigate the impact of Asynchrony change pattern on design and code smells such as anti-patterns and code clones. Concretely, we conduct an empirical study by detecting Asynchrony change patterns, anti-patterns and code clones occurrences on 22 versions of four software systems and analyzing their fault-proneness. Results show that cloned files that follow the same Asynchrony change patterns have significantly increased fault-proneness with respect to other clones, and that anti-patterns following the same Asynchrony change pattern can be up to five times more risky in terms of fault-proneness as compared to other anti-patterns. Asynchrony change patterns thus seem to be strong indicators of fault-proneness for clones and anti-patterns.

A metamodeling approach for the identification of organizational smells in multi-agent systems: application to ASPECS

Software Quality is one of the most important subjects in the Process Development Software, especially in large and complex systems. Much effort has been devoted to the development of techniques and concepts to improve software quality over the years. We are especially interested on smells, which represent anomalies or flaws in the design/code that can have serious consequences in maintenance or future development of the systems. These techniques have a strong development in the Object Oriented paradigm, however, very few studies were conducted in the agent oriented paradigm. In this paper we focus on the detection of design smells applied to multi-agent systems models based on the organizational approach, named Organizational Design Smells (ODS). Early and automatic detection of these ODS allows reducing the costs and development times, while increasing the final product’s quality. To achieve this objective, validation rules were defined based in the EVL language. The approach is illustrated with two examples, their validation rules, and the refactoring solutions proposed.

Investigating the relation between lexical smells and change- and fault-proneness: an empirical study

Past and recent studies have shown that design smells which are poor solutions to recurrent design problems make object-oriented systems difficult to maintain, and that they negatively impact the class change- and fault-proneness. More recently, lexical smells have been introduced to capture recurring poor practices in the naming, documentation, and choice of identifiers during the implementation of an entity. Although recent studies show that developers perceive lexical smells as impairing program understanding, no study has actually evaluated the relationship between lexical smells and software quality as well as their interaction with design smells. In this paper, we detect 29 smells consisting of 13 design smells and 16 lexical smells in 30 releases of three projects: ANT, ArgoUML, and Hibernate. We analyze to what extent classes containing lexical smells have higher (or lower) odds to change or to be subject to fault fixing than other classes containing design smells. Our results show and bring empirical evidence on the fact that lexical smells can make, in some cases, classes with design smells more fault-prone. In addition, we empirically demonstrate that classes containing design smells only are more change- and fault-prone than classes with lexical smells only.

Neural Network based Refactoring Area Identification in Software System with Object Oriented Metrics

Objectives of the Study (a) To study previously designed models for identification of refactoring area in Object Oriented Software Systems. (b) To design a general framework or model that helps to easily identify the software code smells for a good quality of coding. (c) To identify the bad smells in the code with a design of neural network based model with the help of object-oriented metrics and further to predict the performance of the proposed model using various evaluation parameters of confusion matrix. Analysis/Methods: In this study, two different versions of Rhino (1.7r1 and 1.7r2) were taken as dataset. Object –Oriented metrics were taken as input data and the probability factor (occurrence or non-occurrence of a bad smell as output. Presence of a bad smell was considered as 1 and 0 means absence of bad smell. If there was at least one bad smell present in the code in a class, it was marked as smelly class. The tool used to extract the databases for collected object –oriented metrics and bad smells of these Rhino versions is PTIDEJ. Further, the data was tested on neural networks for different epochs to predict their performance. Findings: a) Bad Smell Analysis: Twelve design smells were considered to detect the presence of bad smell in code. If there was at least one bad smell present in the code in a class, it was marked as smelly class. b) Neural Network Model Table: Weight and bias factor for various predictors were calculated for different epochs (500, 1000, and 2000). It shows the weights assigned from input layer to hidden layer and from hidden layer to output neurons layer. After the training, the weights were tested on various datasets. C) Performance Tables and Graphs: In this, the Neural network proposed model was trained using different number of epochs to examine if the number of epochs used in training has any impact on the results or not. Further, the results for the accuracy of these models were shown. Novelty/Improvement: When the data was highly trained then the results were better. When the data was trained with 500 epochs, it was suitable for only with-in company projects but when the data was more trained than the model was also appropriate for cross projects. It was seen that when the data was trained with 1000 and 2000 epochs, the results of the proposed model were improved.

Measuring, Assessing and Improving Software Quality based on Object-Oriented Design Principles

Abstract Good object-oriented design is crucial for a successful software product. Metric-based approaches and the identification of design smells are established concepts for identifying design flaws and deriving design improvements thereof. Nevertheless, metrics are difficult to use for improvements as they provide only weak guidance and are difficult to interpret. Thus, this paper proposes a novel design quality model (DQM) based on fundamental object-oriented design principles and best practices. In course of discussing DQM, the paper provides a contribution in three directions: (1) it shows how to measure design principles automatically, (2) then the measuring result is used to assess the degree of fulfilling object-oriented design principles, (3) and finally design improvements of identified design flaws in object-oriented software are derived. Additionally, the paper provides an overview of the research area by explaining terms used to describe designrelated aspects and by depicting the result of a survey on the importance of object-oriented design principles. The underlying concepts of the DQM are explained before it is applied on two open-source projects in the format of a case study. The qualitative discussion of its application shows the advantages of the automated design assessment that can be used for guiding design improvements.

Refactoring for Software Design Smells

No abstract available.

A Study on Detection of Anti-Patterns in Object-Oriented Systems

Software quality is an important issue in the development of software systems. The extent to which the software possesses a desired set of quality attributes such as testability, performance, maintainability, and manageability indicates the success of the design and the overall quality of the software system. These attributes are adversely affected by antipatterns. These design smells, the symptoms of code smells, are introduced during software development that constrains the evolution of system by making it difficult for engineers to bring changes. Researchers and practitioners put a great effort to detect these anti-patterns to reduce costs, effort and resources. Their detection is important because it allows refactoring or removing them from systems. Consequently, it improves software quality and usability. This paper discusses various manual, semi-automated and SVM based anti-pattern detection techniques for object-oriented systems, so that researchers can get a clear and concise view about them. The limitations and advantages (over previous approaches) of some detection techniques are also compared in this paper.

Towards a Principle-based Classification of Structural Design Smells.

Fred Brooks in his book Mythical Man Month describes how the inherent properties of software (i.e. complexity, conformity, change- ability, and invisibility) make its an essential diculty. Good practices are fundamental requisites to address this diculty. One such good practice is that a software designer should be aware of and address design that can manifest as a result of his decisions. However, our study of the vast literature on object-oriented smells reveals the lack of an eective organization of smells that could better guide a designer in understanding and addressing potential issues in his design. In order to address this gap, we have adopted a novel approach to classify and catalog a number of recurring structural smells based on how they violate key object oriented (OO) principles. To evaluate the usefulness of our smell catalog, we first asked Siemens CT DC AA architects to use it to identify smells in their projects, and later elicited feedback from them about their experience. The feedback received indicates that these architects found the catalog to be very useful. In this paper, we present our catalog, classification, and naming scheme for smells and also highlight several interesting observations and insights that result from our work.

Generating a catalog of unanticipated schemas in class hierarchies using Formal Concept Analysis

Context Inheritance is the cornerstone of object-oriented development, supporting conceptual modeling, subtype polymorphism and software reuse. But inheritance can be used in subtle ways that make complex systems hard to understand and extend, due to the presence of implicit dependencies in the inheritance hierarchy. Objective Although these dependencies often specify well-known schemas (i.e., recurrent design or coding patterns, such as hook and template methods), new unanticipated dependency schemas arise in practice, and can consequently be hard to recognize and detect. Thus, a developer making changes or extensions to an object-oriented system needs to understand these implicit contracts defined by the dependencies between a class and its subclasses, or risk that seemingly innocuous changes break them. Method To tackle this problem, we have developed an approach based on Formal Concept Analysis. Our Formal Concept Analysis based-Reverse Engineering methodology (FoCARE) identifies undocumented hierarchical dependencies in a hierarchy by taking into account the existing structure and behavior of classes and subclasses. Results We validate our approach by applying it to a large and non-trivial case study, yielding a catalog of hierarchy schemas, each one composed of a set of dependencies over methods and attributes in a class hierarchy. We show how the discovered dependency schemas can be used not only to identify good design practices, but also to expose bad smells in design, thereby helping developers in initial reengineering phases to develop a first mental model of a system. Although some of the identified schemas are already documented in existing literature, with our approach based on Formal Concept Analysis (FCA), we are also able to identify previously unidentified schemas. Conclusions FCA is an effective tool because it is an ideal classification mining tool to identify commonalities between software artifacts, and usually these commonalities reveal known and unknown characteristics of the software artifacts. We also show that once a catalog of useful schemas stabilizes after several runs of FoCARE, the added cost of FCA is no longer needed.

DECOR: A Method for the Specification and Detection of Code and Design Smells

Code and design smells are poor solutions to recurring implementation and design problems. They may hinder the evolution of a system by making it hard for software engineers to carry out changes. We propose three contributions to the research field related to code and design smells: (1) DECOR, a method that embodies and defines all the steps necessary for the specification and detection of code and design smells, (2) DETEX, a detection technique that instantiates this method, and (3) an empirical validation in terms of precision and recall of DETEX. The originality of DETEX stems from the ability for software engineers to specify smells at a high level of abstraction using a consistent vocabulary and domain-specific language for automatically generating detection algorithms. Using DETEX, we specify four well-known design smells: the antipatterns Blob, Functional Decomposition, Spaghetti Code, and Swiss Army Knife, and their 15 underlying code smells, and we automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall on XERCES v2.7.0, and discuss the precision of these algorithms on 11 open-source systems.

From a domain analysis to the specification and detection of code and design smells

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

Bad smells in design and design patterns.

To give a consistent and more valuable property on models, model-driven processes should be able to reuse the expert knowledge generally expressed in terms of patterns. We focus our work on the design stage and on the systematically use of design patterns. Choose a good design pattern and ensure the correct integration of the chosen pattern are non trivial for a designer who wants to use them. To help designers, we propose design inspection in order to detect “bad smells in design” and models reworking through use of design patterns. The automatic detection and the explanation of the misconceptions are performed thanks to spoiled patterns. A “spoiled pattern” is a pattern which allows to instantiate inadequate solutions for a given problem: requirements are respected, but architecture is improvable.