In Fig. 2007). One of the aims of this study is to evaluate and compare four code smell detection tools, namely JDeodorant, inFusion, PMD and JSpIRIT. What we haven’t really done is address some of the larger problems, particularly around the domain model being difficult to work with. From the results of Table 5, we made the following observations. On the other hand, the PhotoController.handleCommand method is created in version 2 with a single functionality, saving photo labels. Another observation is that the number of smells does not necessarily grow with the size of the system, even though there was an increase of 2057 lines of code in MobileMedia and of 2706 lines of code in Health Watcher. That is, there is not much variation in the average agreement. Although inFusion and JSpIRIT use the same detection technique and JDeodorant does not, for Health Watcher they yielded similar results. For God Method, JDeodorant reports 100 methods, while the reference list has 67 methods. JDeodorant is again the more aggressive in its detection strategy by reporting 787 instances. We also evaluate inFusion, JDeodorant, and PMD, calculating the agreement among these tools similarly to Fontana et al. ACM, article 18, Figueiredo E, Cacho N, Sant'Anna C, Monteiro M, Kulesza U, Garcia A, Soares S, Ferrari F, Khan S, Castor F, Dantas F (2008) Evolving software product lines with aspects: an empirical study on design stability. The AC1 statistic is also high with most values “Very Good”. Similarly, Health Watcher is a real-life system, but we only had access to a small portion of the source code. (2012). For God Class, JSpIRIT and PMD have similar accuracy, i.e., lower average recalls of 17%, but higher precisions of 67 and 78% when compared to JDeodorant, with a 58% average recall and 28% average precision. Code smells are much more subtle than logic errors and indicate problems that are more likely to impact overall performance quality than cause a crash. Since there are no false negatives or true positives, recall is undefined. Finally, even if the same metrics are used, the threshold values might be different because they are defined considering different factors, such as system domain and its size, organizational practices, and the experience of software engineers that define them. The number of God Classes and God Methods remains constant, with the addition of only one instance of God Class in version 9. The higher standard deviation indicates a greater variation in the agreement between the other tools and JDeodorant from one version to another, when compared with the other pairs of tools. Overall, our results showed that most of the identified code smells in MobileMedia and Health Watcher were already present at the creation of the affected class or method. Study a collection of important Code Smells and compare each one to a simpler, cleaner design. They use, d bad smell taxonomy described in 9] and a bespoke software tool [10] to find number of refactoring [7, required for each of 22 bad smells. Goal: The goal of this paper is to help practitioners avoid … The rest of this paper is organized as follows. It’s about how every single developer writes their code and anyone should aim at writing it right, immediately. Throughout the versions, some God Classes are eliminated by refactoring or by the removal of the class itself. : an exploratory analysis of evolving systems. Here’s a higher-level list of code smells to watch for, in order of priority. The method is noticeably different from all other methods in the same class. On the other hand, it brings a new challenge on how to assess and compare tools and to select the most efficient tool in specific development contexts. To our knowledge, Fontana et al. For all three analyzed smells in Mobile Media, 74.4% (32 of 43) of the smelly classes and methods were smelly from the beginning of their lifetime. Bad code smells can be an indicator of factors that contribute to technical debt. 2012). As coders, we have plenty of lines of code all over the project repository. The method accesses the data of another object more than its own data. This class is an implementation of the Façade design pattern (Gamma et al. In: Proceedings of the 20th international conference on automated software engineering. However, JSpIRIT reported the highest number of methods, reporting 111 methods, while JDeodorant reported 90 and inFusion reported 48. Because – let’s face it – you’ll never have the time to clean it later. Speakers, Join us Tuesday, January 19, 2020, 16:00 - 17:00 CET (10:00 AM - 11:00 AM EST or check other timezones) for our free live webinar, Xamarin, the best way to make NFC Apps, with Saamer Mansoor. Variations in the tools results for MobileMedia and Health Watcher may be related with the fact that these systems are from different domains, Mobile (MobileMedia) and Web (Health Watcher). Learn about NFC technology, potential invention ideas, the NFC capabilities & differences between iPhone & Android apps, and why Xamarin is the best way to make cross-platform NFC Apps. We investigate recall, precision, and agreement of tools in detecting three code smells: God Class, God Method, and Feature Envy. ... Get, Set, Tools: Use more productivity tools and addins that makes your life easier while coding, few of them The columns “Total” indicate the total of smelly classes and methods considering all the versions of the system. However, to reduce this risk we selected systems from different domains, Mobile (MobileMedia) and Web (Health Watcher), which were developed to incorporate nowadays technologies, such as GUIs, persistence, distribution, concurrency, and recurrent maintenance scenarios of real software systems. Without pruning, branches get longer and longer and mostly produce fruit at the tips. All these methods manipulate images and access directly data and methods from one or more classes that also manipulate images, such as the ImageData class. We also intend to investigate more the evolution of other code smells in a system and how their evolution is related to maintenance activities. Since most averages for overall agreement between tools are higher than 80%, we considered that values equal or greater than 80% are high. This allowed the experts to focus on identifying code smell instances instead of trying to understand the system, its dependencies, and other domain-related specificities. One way to deal with this subjectivity is to use machine learning techniques. PubMed Google Scholar. dotCover. For instance, the ImageAccessor and AlbumController classes were created in versions 1 and 4, respectively, as God Classes and remained as such for as long as they are present in the system. For instance, the PhotoController class was created in version 2 without any smell, but it became God Class in version 4 due to the addition of several new functionalities, such as displaying an image on screen and providing the image information. Unlike Fontana et al. Section 3.2 summarizes the reference lists of code smells identified in both systems. The changes include: breaking a single method into multiple methods, adding functionalities, removing functionalities and merging methods. On the other hand, inFusion, JSpIRIT and PMD had higher precision, reporting more correct instances of smelly entities. Therefore, smells should be detected as soon as possible to ease refactoring activities. Section 4.2 analyzes the tools accuracy in detecting code smells from the reference list. https://sites.google.com/site/santiagoavidal/projects/jspirit, Altman DG (1991) Practical statistics for medical research. Code Smells go beyond vague programming principles by capturing industry wisdom about how not to design code. JDeodorant has the highest average recall of 50% and the lowest precision of 35%, values that are further away from the averages of the other tools. Study a collection of important Code Smells and compare each one to a simpler, cleaner design. So much for code smells. 2007). Section 3 describes the study settings focusing on the target systems, code smell reference list, and research questions. Section 5.2 relies on visual representations to show how the code smells evolved in the systems. Thanis Paiva. ber of automatic code smell detection approaches and tools have been developed and validated [20, 24, 37, 39, 52, 62, 64, 68, 71, 89]. The AC1 was also calculated to consider pairs of tools with a 95% confidence interval. This section analyzes variations in the total number of code smell instances in the reference list of MobileMedia and Health Watcher as the systems evolved. J Softw Eng Res Dev 5, 7 (2017). Our study involved ten object-oriented versions (1 to 10) of Health Watcher, ranging from 5 KLOC to almost 9 KLOC. Code smells or bad smells are an accepted approach to identify design flaws in the source code. 2008). By version 4, multiple features were added, such as editing photo labels, sorting photos, and adding photos as favorite, introducing a smell. IEEE, pp 287–296, Marinescu C, Marinescu R, Mihancea PF, Ratiu D, Wettel R (2005) iPlasma: an integrated platform for quality assessment of object-oriented design. In our next post, let’s look at a practical example: special strings! Therefore, it is expected to access data and methods from multiple classes. A white state indicates that the class or method is present in that system version, but it does not have a code smell. Granularity, modularity, separation of concerns, and all the wonderful theoretical concepts we may have heard above become concrete and factual when we get guided by the idea of making our code speak the language of the business. In this article, we present a fexible tool to prioritize technical debt in the form of code smells. The entities classified by both experts as a code smell were registered in the final reference list for each system. In MobileMedia, the pair inFusion-PMD has the highest average agreement (99.66%), followed by the pairs inFusion-JSpIRIT (99.25%) and PMD-JSpIRIT (99.24%). Aside from obvious hygiene and social considerations, in much the same way a strong and unpleasant body smell may be the surface indicator of a deeper medical problem, a strong and unpleasant code smell may be the symptom of relevant weaknesses in the code design. 2016) (Murphy-Hill and Black 2010) (Tsantalis et al. The code smell reference list is a document containing the code smells identified in the source code of a software system. Bloaters are code, methods and classes that have increased to such gargantuan proportions that they are hard to work with. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Originally, 22 code smells were described by Fowler (1999), along with the suggested refactorings. Instead it supports only visualization features. Yourdon, New York, Fernandes E, Oliveira J, Vale G, Paiva T, Figueiredo E (2016) A review-based comparative study of bad smell detection tools. Footnote 3 is an open source tool for Java and an Eclipse plugin that detects many problems in Java code, including two of the code smells of our interest: God Class and God Method. The average recalls of inFusion and of JSpIRIT are lower in Health Watcher for all smells, and for PMD is lower only for God Method. However, the agreement remained high even between tools with distinct techniques, indicating that the results obtained from different techniques are distinct, but still similar enough to yield high agreement values. It aims at answering two research questions to compare the accuracy and agreement of these tools in detecting code smells. Although these tools use the same detection technique and agree on most classes, they disagree on others. Addison-Wesley, Boston, Greenwood P, Bartolomei TT, Figueiredo E, Dosea M, Garcia AF, Cacho N, Sant’Anna C, Soares S, Borba P, Kulesza U, Rashid A (2007) On the impact of aspectual decompositions on design stability: An empirical study. Empir Softw Eng 21(3):1143–1191. This lack of precise definitions implies on tools that implement different detection techniques for the same code smell. iii The only exception is the HealthWatcherFacade class that smells after version 9 with the addition of multiple new functionalities and, consequently, many lines of code. There are no instances of Feature Envy in Health Watcher. The types of problems that can be indicated by a code smell are not usually bugs that will cause an entire system crash – and d evelopers are well trained to uncover logic errors that cause bugs and system failure. Abstract: 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. IEEE, pp 25–30, McCray G (2013) Assessing inter-rater agreement for nominal judgment variables. We then tracked their states throughout the versions of both target systems. Therefore, these systems might not be representative of the industrial practice and our findings might not be directly extended to real large scale projects. However, we used the AC1 statistic, which is more robust than the Kappa Coefficient. JSpIRIT detects a little over twice the amount of actual instances of smells according to the reference list for Health Watcher. We can observe that from version 1 to version 10 there was an increase of 2706 lines of code, with the addition of 41 classes and 270 methods. Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Section 2.2 presents the tools evaluated in this paper. 2008). Evolution of God Method in Health Watcher. In this approach, code smells are detected as agglomerations, unlike our work, where we focus on strategies that identify code smells individually. As a consequence the Code Quality remains under control with no major upfront investment. Our study involved nine object-oriented versions (1 to 9) of MobileMedia, ranging from 1 to over 3 KLOC. Section 4.3 analyzes the agreement among tools. In this paper, we evaluate four code smell detection tools, namely inFusion, JDeodorant, PMD, and JSpIRIT, selected from the tools available for download that are free or have a trial version (Fernandes et al. The tool estimates the Technical Debt progress since the baseline. A higher precision with a lower recall means that the tools do not report some of the affected entities. (more…), Dependency injection doesn’t strictly require frameworks, Null pointers: an opportunity, not an exception, automatic code inspection with ReSharper and Rider, Unity Explorer and new code inspections in Rider 2018.1, The Morning Brew - Chris Alcock » The Morning Brew #2608, Dew Drop - June 19, 2018 (#2749) - Morning Dew. In fact, after comparing the accuracy of MobileMedia and Health Watcher, we found that the precision of all tools for all smells is lower in Health Watcher than in MobileMedia. In: Proceedings of the 20th international conference on evaluation and assessment in software engineering (EASE '16). This nding con rms that the tool should not be completely automated. Finally, JSpIRIT IEEE, pp 35–40, Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A (2012) Experimentation in software engineering. Many interesting tools exist to detect bugs in your C++ code base like cppcheck, clang-tidy and visual studio analyzer. ACM, pp 223–233, Langelier G, Sahraoui HA, Poulin P (2005) Visualization-based analysis of quality for large-scale software systems. We rely on recall and precision to measure their accuracy, while agreement is measured by calculating the overall agreement and the AC1 statistic. On the other hand, if there are time constraints, it can be more important to reduce manual validation effort. Finally, the column Detection Techniques contain a general description of the techniques used by each tool, with software metrics being the most common. 2008). In Health Watcher, there are no instances of Feature Envy. ... Luckily, a new category of tools is emerging called Quality Intelligence Platforms. In a previous work (Paiva et al. 2016). class code smells to do work with. (XLS 148 kb), Complete results and evaluation for Health Watcher. Download Code Bad Smell Detector for free. The first thing you should check in a class is if its name and programming interface reflects its purpose. The complete analysis of the evolution of smelly classes and methods in all versions of MobileMedia and Health Watcher is discussed below using the Figs. 2008). We aim to assess how much the tools agree when classifying a class or method as a code smell. One interesting observation is that when a smell is introduced in a method and then removed in a later version, the method remains non-smelly in the subsequent versions. PMD and JSpIRIT have the same average recall of 17%. 2006) (Kulesza et al. Antônio Carlos, 6627, Belo Horizonte, 31270-901, Brazil, Thanis Paiva, Amanda Damasceno & Eduardo Figueiredo, Department of Computer Science, Federal University of Bahia, Ondina, Salvador, 40170-115, Brazil, You can also search for this author in JDeodorant reports the highest number of God Classes, reporting 98 instances, while PMD and JSpIRIT report lower numbers of classes, 33 and 20. Correspondence to First, we selected the instances of the reference list. Expert Videos: Learn tools & techniques by watching short videos from industry experts. (2012), for instance, investigated six code smells in one software system, named GanttProject. Each rectangle represents the state of the class or method in the system version given by the column. A lower precision and a higher recall increase the validation effort, but capture most the affected entities. The results show that Pysmell can detect 285 code smell instances in total with the average precision of 97.7%. However, detection in large software systems is a time and resource-consuming, error-prone activity (Travassos et al. The subjects of our analysis are the nine versions of the MobileMedia and the ten versions of Health Watcher, which are small size programs. In Health Watcher, the same pairs have the highest averages, nevertheless, the ordering differs, with the pair inFusion-JSpIRIT (97.90%) first, followed by the pairs PMD-JSpIRIT (96.76%) and inFusion-PMD (96.59%). Robert C. Martin calls a list of code smells a “value system” for software craftsmanship. See you next week! The overall agreement considering all the tools is high for all smells, with values over 80% in MobileMedia and over 90% in Health Watcher. ACM, pp 261–270, Fontana FA, Braione P, Zanoni M (2012) Automatic detection of bad smells in code: An experimental assessment. Other smells have also been proposed in the literature, such as Spaghetti Code (Brown et al. PMD is less conservative, detecting a total of 24 instances for God Class and God Method, in contrast with the 20 instances detected by inFusion. Complete results and evaluation for MobileMedia. The default rule-set offers over a hundred code rules that detect a wide range of code smells including entangled code, dead-code, API breaking changes and bad OOP usage. In the online documentation duplicated code is not mentioned. In: Proceedings of the 38th international conference on software engineering. For all smells in both systems, JDeodorant identified most of the correct entities, but reports many false positives. However, no new smelly class or method was introduced. Not all code smells should be “fixed” – sometimes code is perfectly acceptable in its current form. However, only the modifications in version 9 introduced a smell in the class. This paper extends our previous work by including the tool JSpIRIT and the Health Watcher system to increase the confidence of our results and to favor generalization of our findings. And it is often a sore point. Therefore, it is expected that different tools identify different classes and methods as code smells. Springer, pp 176–200, Gwet K (2001) Handbook of inter-rater reliability: how to measure the level of agreement between two or multiple raters. There are a few tools that are dedicatedly developed to detect design smells and improve the quality of the software design. The recall of PMD for God Class also increased: while in MobileMedia the recall is 17%, in Health Watcher the recall is 100%. In: Proceedings of the 12th European conference on software maintenance and reengineering. Details are discussed in the following. Beyond their actual effectiveness, check-in policies can help prevent committing less-than-optimal code. 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 … For instance, the method BaseController.handleCommand was a God Method in versions 1 to 3. Usually these smells do not crop up right away, rather they accumulate over time as the program evolves (and especially when nobody makes an effort to eradicate them). Due to this result, Tufano et al. The class inherits from a base class but only some of the inherited behavior is really needed. JDeodorant is by far the most aggressive in its detection strategies by reporting 254 instances. The complete results for MobileMedia and Health Watcher are available on Additional files 1 and 2. In this second study, we use the code smell reference lists (Section 3.2) to analyze the evolution of code smells in MobileMedia and in Health Watcher. The class depends too much on the implementation details of another class. In the literature, there is no common sense for recall and precision because the acceptable or desirable values depend on several factors, such as the system, the level of quality required, and the maintenance needs of the programmer. For MobileMedia, the same happens for God Method and Feature Envy. The different interpretations of code smell by researchers and developers lead to tools with distinct detection techniques, results, and consequently, the amount of time spent with validation. Equally important are the parameter list and the overall length. The overall agreement among tools varies from 83 to 98% considering all smells in both systems. Infusion works with Java and C/C++ codebase where Designite targets C# code. Paper presented at the Language Testing Forum, University of Nottingham, November 15-17 2013, Moha N, Gueheneuc Y, Duchien L, Le Meur A (2010) DECOR: a method for the specification and detection of code and design smells. Figure 3 show that, despite having more lines of code than MobileMedia, Health Watcher has no instances of Feature Envy. The first thing you should check in a method is its name. God Class defines a class that centralizes the functionality of the system. On the other hand, recall of JDeodorant increased for God Class and God Method, from 58 and 50% in MobileMedia to 70 and 82% in Health Watcher. Addison-Wesley, Boston, Gamma E, Vlissides J, Johnson R, Helm R (1994) Design patterns: elements of reusable object-oriented software. It can indicate that the method is badly located and should be transferred to another class (Fowler 1999). However, we still believe that the agreement can be considered high, just not as high as the agreement among the other pairs of tools that do not include JDeodorant. Journal of Software Engineering Research and Development All these changes lead to the variations in the number of God Methods in the system, either increasing or decreasing the number of smells without a fixed pattern. 2005). On the other hand, JSpIRIT reports 27 God Methods, while PMD and inFusion report similar numbers, 16 and 17, respectively. 2012). In Figs. More simply, a code smell is a piece of code that we perceive as not right, but don’t fix right away. Reporting more correct instances of Feature Envy systems using the four code smell detection by!, doi: 10.1109/tse.2009.50, article 63, Department of computer science, Federal University of Minas Gerais Av! 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Validation effort by reporting 254 instances maintainability: an empirical study report fewer methods, adding functionalities removing. Its current form 85 % when compared to JSpIRIT ( 33 % ) //sites.google.com/site/santiagoavidal/projects/jspirit Altman! Figure 3 show that, despite having more lines of code smells and their informal definition to! Related work while section 8 concludes this paper is that the high agreement also! In different formats single functionality, saving photo labels of accuracy in detecting two code smells creation ” the. 5Th international symposium on empirical software engineering to determine the accuracy was measured by calculating agreement... Work ( Figueiredo et al Gwet 2001 ) white state indicates that the programmer, less false positives imply the! That smells like… napalm in the ten versions of MobileMedia and higher agreement MobileMedia. The beginning and discuss the study, collected the data multiple times informal definition leads to the among. Ha, Poulin P ( 2005 ) ( Murphy-Hill and Black 2010 ) and precision to their! Of Fowler et al method BaseController.handleCommand was a higher recall captures most of the affected entities are.!, GC stands for God class at some of the affected entities providing a Good coverage the... Positives are more desirable longer than 30 lines and doesn ’ t know how to detect smells! Refactoring is indicated by the tool and drafted the manuscript and helped draft and review manuscript! What are the parameter list and the overall agreement ( OA ) considering the on!, many times there is a difficult task because these tools in detecting code smells and the... Some topics around personal development by using check-in policies in visual studio are the smells in smells. ) Practical statistics for medical research findings ( Tufano et al % recall and 0 % recall and precision! Using the four analyzed tools as reported in the identification of code smells ( et.