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. The class unnecessarily exposes its internal details. It can indicate that the method is badly located and should be transferred to another class (Fowler 1999). This code still demonstrates several smells, and can benefit from further refactoring, but it’s a definite improvement on the original. Only the final version has one additional smell instance. In addition, they can be detected by at least two of the evaluated tools. However, JSpIRIT reported the highest number of methods, reporting 111 methods, while JDeodorant reported 90 and inFusion reported 48. We first present in Section 3.1 the selected software systems. Code smells were defined by Kent Beck in Fowler’s book (Fowler 1999) as a mean to diagnose symptoms that may be indicative of something wrong in the system code. In: Proceedings of the 7th international conference on the quality of information and communications technology. In Health Watcher, for God Class and God Method, 7 out of 8 of the smelly classes and methods were smelly from the beginning of their lifetime. Comparing the detected entities with the code smell reference list, we concluded that the presence of a smell in a system can be considered somewhat a rare occurrence and this seems to be confirmed by the results of tools. On the other hand, the PhotoController.handleCommand method is created in version 2 with a single functionality, saving photo labels. Refactoring is the process of improving the quality of the code without altering its external behavior. However, more smelly classes were created than removed, leading to an increase in God Classes as the system evolves. Each month, we try to bring you at least one session where community speakers cover the topics they are passionate about. Section 4.2 analyzes the tools accuracy in detecting code smells from the reference list. These tools were applied to different versions of the same software systems, namely MobileMedia and Health Watcher, to calculate the accuracy and agreement of code smell detection tools. 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. 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. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Table 11 summarizes quantitatively our findings of the evolution of code smells in MobileMedia (MM) and Health Watcher (HW). 2015). For MobileMedia, the same happens for God Method and Feature Envy. The AC1 statistic, or first-order agreement coefficient, is the conditional probability that two randomly selected raters agree given that there is no agreement by chance (Gwet 2001). A great month! JetBrains Webinars? Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Footnote 1 is still available. In: Proceedings of the 2005 international symposium on empirical software engineering. doi:10.1002/spe.715, Travassos G, Shull F, Fredericks M, Basili VR (1999) Detecting defects in object-oriented designs: using reading techniques to increase software quality. The growth of a code base can be compared to the growth of a tree. But it indicates a violation of design principles that might lead to problems further down the road. Figure 3 show that, despite having more lines of code than MobileMedia, Health Watcher has no instances of Feature Envy. In our work, we also analyze the evolution of code smells, but at a higher level and neither focused on maintenance activities and refactoring, like Chatzigeorgiou and Manakos (2010), nor in the reasons why the smells were introduced, like Tufano et al. An semi-automated tool is best suited for this purpose. Is clearly and appropriately named 2. For the next 10 weeks, we’ll have weekly posts by Dino Esposito (@despos) around a common theme: code smells and code structure. Although these tools use the same detection technique and agree on most classes, they disagree on others. : an exploratory analysis of evolving systems. This section aims to track instances of code smells from MobileMedia and Health Watcher in two steps. 2. inFusion has the lowest average recall of 9%, however, it has an average precision of 33%, 5% higher than JDeodorant (28%). Section 5.1 presents the number of code smells identified manually by experts in all versions of each system. Furthermore, we intend to investigate the influence of different domains in the analysis of detection tools. In this approach, code smells are detected as agglomerations, unlike our work, where we focus on strategies that identify code smells individually. An overview of the tables shows that the minimum average recall is 0% and the maximum is 100%, while the minimum average precision is 0% and the maximum 85%. This relates to the naming convention, the (spoken) language in which naming is expressed, and imperative approach. Typically, the ideal method: 1. 2006) (Greenwood et al. intents. Therefore, tools with higher precision and, therefore, that report less false positives are more desirable. JDeodorant is again the more aggressive in its detection strategy by reporting 787 instances. We can observe that from versions 1 to 9 there was an increase of 2057 lines of code, 31 classes, and 166 methods. Therefore, recall is more relevant and a higher recall is desirable. The former was created in the first version of the system, already as a God Class, and it remained as such throughout the entire evolution of the system. Changing the thresholds has a large impact on the number of code smells detected (Vale et al. doi:10.1901/jaba.1977.10-103, House AE, House BJ, Campbell MB (1981) Measures of interobserver agreement: Calculation formulas and distribution effects. 2008). Solution Sprawl, Contrived Complexity, and even Oddball Solutions can be easily added with the best intentions during refactoring especially if the vision of the entire project is limited. It was published as 119 code smells, even though there were 120 of them at the time. 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. For God Method, JDeodorant reports a very high number, 599 methods, while the reference list contains 60 methods. Keep an eye on our blog! Therefore, in Health Watcher almost all smells were introduced at the creation of the class or method. Code smells are code fragments that suggest the possibility of refactoring. This fact seems to support our analysis that for God Class, the detection technique of JDeodorant, when compared to the other tools, leads to different levels of agreement. They’re a diagnostic tool used when considering refactoring software to improve its design. 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. Different tools implement different detection techniques and sometimes the same technique can be implemented with variations specific to a particular tool, such as different threshold values. 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. The closer we can move the expressiveness of the programming language to the business, the more readable and granular our code becomes. ACM, pp 167–178, Mäntylä MV (2005) An experiment on subjective evolvability evaluation of object-oriented software: explaining factors and inter-rater agreement. On the other hand, also in version 4, the new AlbumController class has already been created as God Class. In addition, from versions 4 to 7, one God Class is introduced per version and two are added in version 8. However, the overall agreement considering all versions can still be considered high, ranging from 67.57 to 82% in MobileMedia and from 84.42 to 91.30% in Health Watcher. ACM, pp 176–185, Vidal S, Vázquez H, Díaz-Pace A, Marcos C, Garcia A, Oizumi W (2015) JSpIRIT: a flexible tool for the analysis of code smells. EF provided guidance for the study design, for the analysis, and helped draft and review the manuscript. The standard deviation has a minimum of 0.676 and maximum of 0.980, meaning there is not much variation of the agreement across the versions of the system. If a tool provides the detection of the code smells, it must provides also the possibility to customize it. The risk of “deodorant comments” is that you use comments to smooth the natural (bad) odor of the code. In: European conference on object-oriented programming. For Feature Envy, JSpIRIT reports 74 methods, the highest number, followed by JDeodorant reporting 69 methods, while the reference list contains 19 methods. In Fig. ber of automatic code smell detection approaches and tools have been developed and validated [20, 24, 37, 39, 52, 62, 64, 68, 71, 89]. However, further investigation is necessary to determine the influence of the domain in the tools results. PubMed Google Scholar. 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. 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. This section summarizes the code smells detected in the two target systems using the four analyzed tools. (2015) focused in identifying when and why smells are introduced in the system in a large empirical study of 200 open source projects. A code smell is not the same as a bug. As emphatic as it may sound, comments should never state the obvious. It provides us with some downtime, there are a number of holidays, and it's the month where I turn 37. In the online documentation duplicated code is not mentioned. 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. 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. The similar role of image manipulation might have made it difficult for the developers to identify the correct class where the methods should have been placed and, consequently, they introduced Feature Envy instances in the system. About transcription errors, the tools analyzed generate outputs in different formats. In general, this fact indicates an awareness of the software engineering community about the importance of controlling the structural quality of features under development (Fontana et al. However, in version 4, the method was broken into other non-smelly methods, contributing to the decrease of smells. J Appl Behav Anal 10:103–116. IEEE press, pp 403–414, Vale G, Albuquerque D, Figueiredo E, Garcia A (2015) Defining metric thresholds for software product lines: a comparative study. The standard deviation between JDeodorant and the other tools is also higher than the other pairs of tools, with a minimum of 3.508 and a maximum of 3.729 in MobileMedia and a minimum of 0.914 and a maximum of 1.880 in Health Watcher. Correspondence to By using this website, you agree to our The reference list has only 12 God Classes, while the tools report more instances, except inFusion that reports none. 2015), this paper aims at increasing our knowledge about code smells and their detection tools. 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. Lastly, inFusion reports only 9 instances of Feature Envy. 6, we observe that for Feature Envy, in 3 out of 4 instances, the smell originated with the method and persisted during its entire existence. Addison-Wesley, Boston, Soares S, Borba P, Laureano E (2006) Distribution and persistence as aspects. Unlike Fontana et al. What are the smells that you commonly see in Java code that are missing in this list? The method depends too much on the implementation details of another method or another class. Too simple, primitive types are used to model data with some special meaning. Refactoring and smells have been well researched by the software-engineering research community these past decades. Some limitations are typical of studies like ours, so we discuss the study validity with respect to common threats to validity. The changes include: breaking a single method into multiple methods, adding functionalities, removing functionalities and merging methods. 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