An Empirical Investigation of Code Smell 'Deception' and Research Contextualisation through Paul's Criteria (original) (raw)
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Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations
Code Smells and Refactoring: A Tertiary Systematic Review of Challenges and Observations, 2020
Refactoring and smells have been well researched by the software-engineering research community these past decades. Several secondary studies have been published on code smells, discussing their implications on software quality,their impact on maintenance and evolution, and existing tools for their detection. Other secondary studies addressed refactoring, discussing refactoring techniques, opportunities for refactoring, impact on quality, and tools support. In this paper, we present a tertiary systematic literature review of previous surveys, secondary systematic literature reviews, and systematic mappings. We identify the main observations (what we know) and challenges (what we do not know) on code smells and refactoring. We perform this tertiary review using eight scientific databases, based on a set of five research questions, identifying 40 secondary studies between 1992 and 2018. We organize the main observations and challenges about code smell and their refactoring into: smells definitions, most common code-smell detection approaches, code-smell detection tools, most common refactoring, and refactoring tools. We show that code smells and refactoring have a strong relationship with quality attributes, i.e., with understandability, maintainability, testability, complexity, functionality, and reusability. We argue that code smells and refactoring could be considered as the two faces of a same coin. Besides, we identify how refactoring affects quality attributes, more than code smells. We also discuss the implications of this work for practitioners, researchers, and instructors. We identify 13 open issues that could guide future research work. Thus, we want to highlight the gap between code smells and refactoring in the current state of software-engineering research. We wish that this work could help the software-engineering research community in collaborating on future work on code smells and refactoring.
An Uncovering of Bad Smell in Software Refactoring
2013
This paper discusses refactoring, which is one of the techniques to keep software maintainable. However, refactoring itself will not bring the full benefits, if we do not understand when refactoring needs to be applied. To make it easier for a software developer to decide whether certain software needs refactoring or not, Fowler & Beck (Fowler & Beck 2000) give a list of bad code smells. Bad smells are signs of potential problems in code. Detecting and resolving bad smells, however, remain time-consuming for software engineers despite proposals on bad smell detection and refactoring tools. Numerous bad smells have been recognized, yet the sequences in which the detection and resolution of different kinds of bad smells are performed are rarely discussed because software engineers do not know how to optimize sequences or determine the benefits of an optimal sequence. To this end, we propose a detection and resolution sequence for different kinds of bad smells to simplify their detecti...
Exploring the Eradication of Code Smells: An Empirical and Theoretical Perspective
Advances in Software Engineering, 2010
Code smells reflect code decay, and, as such, developers should seek to eradicate such smells through application of “deodorant” in the form of one or more refactorings. However, a relative lack of studies exploring code smells either theoretically or empirically when compared with literature on refactoring suggests that there are reasons why smell eradication is neither being applied in anger, nor the subject of significant research. In this paper, we present three studies as supporting evidence for this stance. The first is an analysis of a set of five, open-source Java systems in which we show very little tendency for smells to be eradicated by developers; the second is an empirical study of a subsystem of a proprietary, C# web-based application where practical problems arise in smell identification and the third, a theoretical enumeration of smell-related refactorings to suggest why smells may be left alone from an effort perspective. Key findings of the study were that first, s...
Understanding the impact of refactoring on smells: a longitudinal study of 23 software projects
Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering
Code smells in a program represent indications of structural quality problems, which can be addressed by software refactoring. However, refactoring intends to achieve different goals in practice, and its application may not reduce smelly structures. Developers may neglect or end up creating new code smells through refactoring. Unfortunately, little has been reported about the beneficial and harmful effects of refactoring on code smells. This paper reports a longitudinal study intended to address this gap. We analyze how often commonly-used refactoring types affect the density of 13 types of code smells along the version histories of 23 projects. Our findings are based on the analysis of 16,566 refactorings distributed in 10 different types. Even though 79.4% of the refactorings touched smelly elements, 57% did not reduce their occurrences. Surprisingly, only 9.7% of refactorings removed smells, while 33.3% induced the introduction of new ones. More than 95% of such refactoringinduced smells were not removed in successive commits, which suggest refactorings tend to more frequently introduce long-living smells instead of eliminating existing ones. We also characterized and quantified typical refactoring-smell patterns, and observed that harmful patterns are frequent, including: (i) approximately 30% of the Move Method and Pull Up Method refactorings induced the emergence of God Class, and (ii) the Extract Superclass refactoring creates the smell Speculative Generality in 68% of the cases.
An Analytical Study of Code Smells
Tehnički glasnik, 2021
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's 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, actiona...
How developers perceive smells in source code: A replicated study
Context. In recent years, smells, also referred to as bad smells, have gained popularity among developers. However, it is still not clear how harmful they are perceived from the developers' point of view. Many developers talk about them, but only few know what they really are, and even fewer really take care of them in their source code. Objective. The goal of this work is to understand the perceived criticality of code smells both in theory, when reading their description, and in practice. Method. We executed an empirical study as a differentiated external replication of two previous studies. The studies were conducted as surveys involving only highly experienced developers (63 in the first study and 41 in the second one). First the perceived criticality was analyzed by proposing the description of the smells, then different pieces of code infected by the smells were proposed, and finally their ability to identify the smells in the analyzed code was tested. Results. According to our knowledge, this is the largest study so far investigating the perception of code smells with professional software developers. The results show that developers are very concerned about code smells in theory, nearly always considering them as harmful or very harmful (17 out of 23 smells). However, when they were asked to analyze an infected piece of code, only few infected classes were considered harmful and even fewer were considered harmful because of the smell. Conclusions. The results confirm our initial hypotheses that code smells are perceived as more critical in theory but not as critical in practice.
Do Developers Care about Code Smells? An Exploratory Survey
20th Working Conference on Reverse Engineering (WCRE 2013)
Code smells are a well-known metaphor to describe symptoms of code decay or other issues with code quality which can lead to a variety of maintenance problems. Even though code smell detection and removal has been well-researched over the last decade, it remains open to debate whether or not code smells should be considered meaningful conceptualizations of code quality issues from the developer's perspective. To some extend, this question applies as well to the results provided by current code smell detection tools. Are code smells really important for developers? If they are not, is this due to the lack of relevance of the underlying concepts, due to the lack of awareness about code smells on the developers' side, or due to the lack of appropriate tools for code smell analysis or removal? In order to align and direct research efforts to address actual needs and problems of professional developers, we need to better understand the knowledge about, and interest in code smells, together with their perceived criticality. This paper reports on the results obtained from an exploratory survey involving 85 professional software developers.
Research Trends, Detection Methods, Practices, and Challenges in Code Smell: SLR
IEEE Access
A code smell indicates a flaw in the design, implementation, or maintenance process that could degrade the software's quality and potentially cause future disruptions. Since being introduced by Beck and Fowler, the term code smell has attracted several studies from researchers and practitioners. However, over time, studies are needed to discuss whether this issue is still interesting and relevant. Objective: Conduct a thorough systematic literature review to learn the most recent state of the art for studying code smells, including detection methods, practices, and challenges. Also, an overview of trends and future relevance of the topic of code smell, whether it is still developing, or if there has been a shift in the discussion. Method: The search methodology was employed to identify pertinent scholarly articles from reputable databases such as ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, ProQuest, and CiteSeerX. The application of inclusion and exclusion criteria serves to filter the search results. In addition, forward and backward snowballing techniques are employed to enhance the comprehensiveness of the results. Results: The inquiry yielded 354 scholarly articles published over the timeframe spanning from January 2013 to July 2022. After inclusion, exclusion, and snowballing techniques were applied, 69 main studies regarding code smells were identified. Many researchers focus on detecting code smells, primarily via machine learning techniques and, to a lesser extent, deep learning methods. Additional subjects encompass the ramifications of code smells; code smells within specific contexts, the correlation between code smells and software metrics, and facets about security, refactoring, and development habits. Contexts and types of code smells vary in the focus of the study. Some tools used are Jspirit, aDoctor, CAME, and SonarQube. The study also explores the concept of design smells and anti-pattern detection. While a singular dominating technique to code smell detection has yet to be thoroughly investigated, other aspects of code smell detection remain that still need to be examined. Conclusion: The findings underscore scholarly attention's evolution towards code smells over the years. This study identified significant journals and conferences and influential researchers in this field. The detection methods used include empirical, machine learning, and deep learning. However, challenges include subjective interpretation and limited contextual applicability.
Detection of Bad Smell Code for Software Refactoring
Innovations in Computer Science and Engineering, 2018
This paper discusses refactoring, which is one of the techniques to keep software maintainable. However, refactoring itself will not bring the full benefits, if we do not understand when refactoring needs to be applied. To make it easier for a software developer to decide whether certain software needs refactoring or not, Fowler & Beck (Fowler & Beck 2000) give a list of bad code smells. Bad smells are signs of potential problems in code. Detecting and resolving bad smells, however, remain time-consuming for software engineers despite proposals on bad smell detection and refactoring tools. Numerous bad smells have been recognized, yet the sequences in which the detection and resolution of different kinds of bad smells are performed are rarely discussed because software engineers do not know how to optimize sequences or determine the benefits of an optimal sequence. To this end, we propose a detection and resolution sequence for different kinds of bad smells to simplify their detection and resolution. We highlight the necessity of managing bad smell resolution sequences with a motivating example, and recommend a suitable sequence for commonly occurring bad smells. We evaluate this recommendation on two nontrivial open source applications, and the evaluation results suggest that a significant reduction in effort ranging from 17.64 to 20 percent can be achieved when bad smells are detected and resolved using the proposed sequence.
Empirical Software Engineering, 2013
Code smells are indicators of deeper design problems that may cause difficulties in the evolution of a software system. This paper investigates the capabil- ity of twelve code smells to reflect actual maintenance problems. Four medium-sized systems with equivalent functionality but dissimilar design were examined for code smells. Three change requests were implemented on the systems by six software de- velopers, each of them working for up to four weeks. During that period, we recorded problems faced by developers and the associated Java files on a daily basis. We developed a binary logistic regression model, with “problematic file” as the depen- dent variable. Twelve code smells, file size, and churn constituted the independent variables. We found that violation of the Interface Segregation Principle (a.k.a. ISP violation) displayed the strongest connection with maintenance problems. Analysis of the nature of the problems, as reported by the developers in daily interviews and think-aloud sessions, strengthened our view about the relevance of this code smell. We observed, for example, that severe instances of problems relating to change propagation were associated with ISP violation. Based on our results, we recommend that code with ISP violation should be considered potentially problematic and be prioritized for refactoring.