466–475. the code element which does not correspond to a real-world scenario. cross-validation, using ten repetitions. 1–13. Proceedings of the 28th IEEE/ACM International Conference on Automated ∙ In the future, we want to detect other method level code smells also. Is clearly and appropriately named 2. code: An experimental assessment., Journal of Object Technology 11 (2) (2012) Code smell is a symptom in the source code that indicates a deeper problem. We applied, two multilabel classification methods on the dataset. Proposed approach detected only two smells, and it is not limited. maintain. 0 612–621. 28 (6) (2011) 96–99. To test the performance of the different code smell prediction models built, we apply 10-fold cross validation and run them up to 10 times to cope with randomness hall2011developing . These 132 and 125 instances are suffered from disparity i.e., same instance is having two class label (smelly and non-smelly). ∙ In literature azeem2019machine , code smell detection were single label (binary) classifiers, used to detect the single type code smell (presence or absence) only. D. Bowes, D. Randall, T. Hall, The inconsistent measurement of message chains, The main difference between MLC and existing approaches is that the expected output from the trained models. In this paper, these common instances are led to construct the MLD and also to avoid the disparity. De Lucia, Detecting Code smell differs from project to project and developer to developer, according to the design standards that have been set by an organization. Conference on, IEEE, 2004, pp. Code smells can be easily detected with the help of tools. TOOLS 30 Proceedings, IEEE, 1999, pp. With this, the prepared multilabel dataset is well balanced because of the MeanIR value in our case is 1.0 which is less than the 1.5. In addition, the authors built four datasets, one for each smell. 02/08/2019 ∙ by Thirupathi Guggulothu, et al. Then, we used single label ML techniques (tree based classifiers) on those datasets. Read, P. Reutemann, B. Pfahringer, G. Holmes, Meka: a The goal of this thesis project was to develop a prototype of a code smell detection plug-in for the Eclipse IDE framework. Classifier Chains (CC) read2011classifier : The algorithm tries to enhance BR by considering the label correlation. Management (ICCTIM2014), The Society of Digital Information and Wireless QSIC’09. 1063–1088. J. Software 84 (4) (2011) 559–572. The CC method has given best performance than LC based on all three measures. While merging FE into LM, there are 395 common instances among which 132 are smelly instances in LM dataset. Label based measures would fail to directly address the correlations among different classes. Code smells are characteristics of the software that indicates a code or design problem which can make software hard to understand, evolve, and maintain. Request A Demo . In the existing study, the performance of all models got an average 73% accuracy, whereas in proposed study we got an average 91%. fowler1999refactoring , have defined 22 informal code smells. tempero2010qualitas . Label based metrics are computed for each label instead of each instance. The removal of disparity instances datasets are avaliable for download at https://github.com/thiru578/Datasets-LM-FE. This design pattern also enables the seamless addition of new smell detectors in the future. learning techniques, Knowledge-Based Systems 128 (2017) 43–58. The authors have sampled 398 files and 480 method levels pairs across 8 real world java software system. Uses the simplest possible way to do its job and contains no dead code Here’s a list of code smells to watch out for in methods, in order of priority. We measured average accuracy, hamming loss, and an exact match of those 100 iterations. A. Rao, K. N. Reddy, Detecting bad smells in object oriented design using Amorim et al. X. Wang, Y. Dang, L. Zhang, D. Zhang, E. Lan, H. Mei, Can i clone this piece of In this paper we introduce ADOCTOR (AnDrOid Code smell detecTOR), a novel code smell detector that identifies 15 multilabel classification: Measures and random resampling algorithms, Detecting bad smells in source code using change history information, in: Prediction, LVMapper: A Large-variance Clone Detector Using Sequencing Alignment When it comes to code smell prioritization, however, the re-search contribution so far is notably less prominent and much more focused on the idea of ranking refactoring recommendations. 06/17/2020 ∙ by Rudolf Ferenc, et al. Starting from an initial set of 2456 papers, we found that 15 of them actually adopted machine learning approaches. Despite their good accuracy, previous work pointed out three important limitations that might preclude the use of code smell detectors in practice: (i) subjectiveness of developers with respect to code smells detected by such tools, (ii) scarce agreement between different detectors, and (iii) difficulties in finding good thresholds to be used for detection. ICSM’05. ch... To detect large-variance code clones (i.e. Usually, the considered code smells co-occur each other palomba2017investigating . Software Reliability Engineering (ISSRE), 2015 IEEE 26th International E. Tempero, C. Anslow, J. Dietrich, T. Han, J. Li, M. Lumpe, H. Melton, The tool at this point is capable of detecting the following code smells in JavaScript: Closure smells , introduce SVMDetect, an approach to detect anti-patterns, based on support vector machines. code clones based on machine learning, Empirical Software Engineering 20 (4) 8–13. The two method level code smells used to detect them are long method and feature envy. That is, for every instance there can be one or more labels associated with them. J. Noble, The qualitas corpus: A curated collection of java code for Several algorithms developed under BR and LP methods. Let C1,C2…Cn be the sum of complexity. This makes the datasets unrealistic i.e., a software system usually contains different types of smells and might have made easier for the classifiers to discriminate smelly instances. The MLD also maintain similar characteristics as in the modified datasets of di2018detecting , like metric distribution and have different types of smells. code here?, in: Proceedings of the 27th IEEE/ACM International Conference on Computer Science 141 (4) (2005) 117–136. So your code is showing a few flaws, but not enough to be considered a bug. Code smells are characteristics of the software that indicates a code or O. Ciupke, Automatic detection of design problems in object-oriented 03/29/2018 ∙ by Ihab S. Mohamed, et al. In addition, the importance of multilabel classification for code smell can identify the critical code elements (method or class) which are urgent need of refactoring. M. White, M. Tufano, C. Vendome, D. Poshyvanyk, Deep learning code fragments ∙ 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). After that, we used the same tree-based classifiers as in the di2018detecting on the removal disparity instances datasets and achieved 95% and 98% accuracy in LM and FE respectively. Table 4 lists the basic measures of multi-label training dataset characteristics. smells, in: Proceedings of the 5th international symposium on Software Some of the basic measures in single label dataset are attributes, instances, and labels. Earlier the performance on long method and feature envy datasets were an average 73% and 75% using tree based classifier. 2011, pp. Automated Software Engineering, ACM, 2012, pp. Communication, 2014, pp. Code smell detection tools can help developers to maintain software quality by employing different techniques for detecting code smells, such as object-oriented metrics (Lanza and Marinescu 2006) and program slicing (Tsantalis et al. These instances led to an idea to form multilabel dataset. Where boundary between smelly and non smelly characteristics is not always clear in real case tufano2017and , fontana2016antipattern . We’ll show you. But, in the proposed study we detected two smells in the same instance and obtained 91% of accuracy. A. visualization, ACM, 2010, pp. In this paper, there have been two algorithms which covering these methods: Classifier chains (CC) under BR category and LC aka LP category. In this work, multilabel classifiers are used to detect the multiple code smells for the same element. Decision Trees and Support Vector Machines are the most commonly used machine learning algorithms for code smell detection. Is no longer than 30 lines and doesn’t take more than 5 parameters 3. After the transformation, we used top 5 tree based (single label) classifiers for the predictions of multilabel methods (CC, LC). J. Yang, K. Hotta, Y. Higo, H. Igaki, S. Kusumoto, Classification model for fault-prediction models: What the research can show industry, IEEE software H. Liu, X. Guo, W. Shao, Monitor-based instant software refactoring, IEEE Networks, An ensemble learning approach for software semantic clone detection, Detection, localisation and tracking of pallets using machine learning Software: Evolution and Process 27 (11) (2015) 867–895. J. The International Conference on Computing Technology and Information In ML, classification problems can be classified into three main categories: Binary (yes or no), MultiClass and Multilabel classification (MLC). The reason for choosing these algorithms is that they capture the label dependencies (correlation or co-occurrence) during classification is thus leading to improve the classification performance guo2011multi . share, Source code clones are categorized into four types of increasing difficu... 9th International Conference on, IEEE, 2009, pp. Feature Envy (FE): Feature Envy is the method level smell which uses more data from other classes rather than its own class i.e., it accesses more foreign data than the local one. As a final step, the sampled dataset was normalized for size: the authors randomly removed smelly and non-smelly elements building four disjoint datasets, i.e., one for each code smell type, composed of 140 smelly instances and 280 non-smelly ones (for a total of 420 elements). , introduces an adaptive detection to combine known methods for finding design flaws viz., Big Class (Large Class) and Long Method on the basis of metrics with learning decision trees. However, code smell detectors cannot usually achieve 100% recall, meaning that an automatic detection process might not identify actual code smell instances (i.e., false positives) even in the case that Table I DETECTORS CONSIDERED FOR BUILDING A CODE SMELL DATASET. Chidember and kemerer proposed a six metric suite used for analyzing the proposed variable. W. Abdelmoez, E. Kosba, A. F. Iesa, Risk-based code smells detection tool, in: However, the tool is able to detect a limited number of the Android-specific code smells defined by Reimann et al. To over come the above limitations, Di Nucci et al. 350–359. N. Moha, Y.-G. Gueheneuc, A.-F. Duchien, et al., Decor: A method for the classification, Pattern recognition 37 (9) (2004) 1757–1771. This increases the functional complexity of the method and it will be difficult to understand. ∙ Code smells are characteristics of the software that indicate a code or design problem which can make software hard to understand, evolve, and maintain. After removal of disparity instances in both the datasets, now we got an average 95%, 98%. In the following subsections, we explain the procedure of constructed MLD and methods used for experimentation of multiple label classification. To clean up code smells, one must refactor. using machine learning techniques, in: Computer Science and Software 18–32. G. Tsoumakas, I. Katakis, Multi-label classification: An overview, Proceedings. , three classification types were used in the code smell detection: 1) binary code smell (presence or absence) 2) based on probability 3) based on severity. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. yang2015classification , study the judgment of individual users by applying machine learning algorithms on code clones. The merged datasets are listed in Table 2. share, Code clones are duplicate code fragments that share (nearly) similar syn... In existing literature, these datasets are used as a single label methods. However, these tools are … To address the issue of tool subjectivity, machine learning techniques fontana2016comparing , that the prepared datasets do not represent a real world scenario. M. S. Sorower, A literature survey on algorithms for multi-label learning, MLC is frequently used in some application areas like multimedia classification, medical diagnosis, text categorization, and semantic scene classification. 261–269. M. Fowler, K. Beck, J. Brant, W. Opdyke, D. Roberts, Refactoring: Improving the When concerned individually there are 140 instances affected by LM and FE. The analyses also reveal the existence of several open issues and challenges that the research community should focus on in the future. But what is a code smell and how do you find it? The findings coming from RQ0 clearly point out the high imbalance between classes affected and not by code smells. Long Method (LM): A code smell is said to be long method when it has more number of lines in the code and requires too many parameters. white2016deep. (2) Label power set(LP) method boutell2004learning : is used to convert MLD to Multi-class dataset based on the label set of each instance as a class identifier. share, The problem of autonomous transportation in industrial scenarios is rece... That is, we are classifying the critical element by using multilabel classification based on the number of code smell detected by the element in the dataset. 0 These metrics became features for independent variables in the datasets. Software Engineering (TSE) 36 (1) (2010) 20–36. T. Hall, S. Beecham, D. Bowes, D. Gray, S. Counsell, Developing WMC=Σ M. 2. In this RQ2: What would be the performance improvement after removing the disparity instances? Y. Guo, S. Gu, Multi-label classification using conditional dependency In this section, we consider only machine learning-based approaches for detecting the code smells. Research 17 (1) (2016) 667–671. For this work, we considered two method datasets which are constructed by single type detectors. In this paper, we addressed the disparity instances and due to this the performances decreased in Di Nucci et al. A code clone is a pair of code fragments, within or between software sys... Bugs are inescapable during software development due to frequent code share. Maintenance, 2005. D. Di Nucci, F. Palomba, D. A. Tamburri, A. Serebrenik, A. 0 (1) Binary relevance (BR) method godbole2004discriminative : it will convert an MLD to as many binary datasets as the number of different labels that are present. code smells using machine learning techniques: are we there yet?, in: 2018 design of existing programs (1999). , propose an approach that assists in understanding the harmfulness of intended cloning operations using Bayesian Networks and a set of features such as history, code, destination features. opportunities, IEEE Transactions on Software Engineering 35 (3) (2009) In the same way, when LM is merged with FE, there are 125 smelly instances in FE dataset. di2018detecting , modified the datasets of Fontana et al. 22, 2011, p. 1300. The open issues emerged in this study can represent the input for researchers interested in developing more powerful techniques. The merged datasets have reduced the metric distribution and contains more than one type of smell instances. ∙ Internally, tsDetect initially calls the JavaParser library to parse the source code files. There is a drastic change in the performance after removal of disparity. A. Maiga, N. Ali, N. Bhattacharya, A. Sabane, Y.-G. Gueheneuc, E. Aimeur, The evaluation metric of MLC is different from that of single label classification, since for each instance there are multiple labels which may be classified partly correctly or partly incorrectly. Several code smells detection tools have been developed providing different results, because smells can be subjectively interpreted and hence detected in different ways. paper, we have used multilabel classification methods to detect whether the These datasets are available at http://essere.disco.unimib.it/reverse/MLCSD.html. reengineering, in: Technology of Object-Oriented Languages and Systems, 1999. In both the tables, it is shown that random forest classifier is giving the best performance based on all three measures. You just have to trust your instinct and do as Frank Farmer said in the comments above. 278–281. As a general rule, charte2015addressing any MLD with a MeanIR value higher than 1.5 should be considered as imbalanced. In a real-world scenario, a code element can contain more than one design problems (code smells) and our MLD constructed accordingly. The two labels will have four label combinations (label sets) in our dataset. Background: Code smells indicate suboptimal design or implementation choices in the source code that often lead it to be more change- and fault-prone.Researchers defined dozens of code smell detectors, which exploit different sources of information to support developers when … W. Kessentini, M. Kessentini, H. Sahraoui, S. Bechikh, A. Ouni, A cooperative experimenting machine learning techniques for code smell detection, Empirical In this paper, we identified the disparity instances in the merged datasets and removed them by manual process. di2018detecting . M. R. Boutell, J. Luo, X. Shen, C. M. Brown, Learning multi-label scene Code smells are patterns in programming code which indicate potential issues with software quality. L. Amorim, E. Costa, N. Antunes, B. Fonseca, M. Ribeiro, Experience report: Copyright © 2020 Elsevier B.V. or its licensors or contributors. Conference on Automated Software Engineering, ACM, 2016, pp. in: Software Analysis, Evolution, and Reengineering (SANER), 2016 IEEE 23rd That is, if an element can be affected by more design problems then this element given has the highest priority for refactoring. detection, in: Automated Software Engineering (ASE), 2012 Proceedings of the N. Maneerat, P. Muenchaisri, Bad-smell prediction from software design model For example, if there are two code smells in the same method, then this method is suffering from more design problems (critical) associated to those code smells rather than single code smell. R. Marinescu, Detection strategies: Metrics-based rules for detecting design fontana2016comparing , have analyzed Qualitus Corpus software systems which are collected from Tempero et al. LC aka LP (Label Powerset) Method boutell2004learning : Treats each label combination as a single class in a multi-class learning scheme. JSNose is a JavaScript code smell detector tool written in Java. Fontana et al. To answer RQ2, We have removed 132, and 125 disparity instances of LM and FE merged datasets respectively. MLC is a way to learn from instances that are associated with a set of labels (predictive classes). The analyses were conducted on two software systems known as: IYC system and the WEKA package. Asia Pacific, IEEE, 2010, pp. In the following subsections, we briefly describe the data preparation methodology of Fontana et al. In the existing study, the performances were an average 76% accuracy and detected only one type of smell. 331–336. According to Kessentini et al. Smurf: A svm-based incremental anti-pattern detection approach, in: Reverse After observing the results, authors have suggested that ML algorithms are most suitable approach for the code smell detection. According to kessentini et al. F. A. Fontana, J. Dietrich, B. Walter, A. Yamashita, M. Zanoni, Antipattern and Proceedings of the 21st IEEE International Then, two MLC methods used on the MLD. These datasets represented the training set for the ML techniques. Determining what is and is not a code smell is subjective, and varies by language, developer, and development methodology. design problem which can make software hard to understand, evolve, and To answer the RQ1, we have considered the configured datasets of di2018detecting . Consequently, developers may identify refactoring opportunities by detecting code smells. However, the clones with relatively more Out of 445, 85 instances are affected by both the smells. One way to remove them is by using refactoring techniques opdyke1992refactoring . In addition to these results, we also listed other metrics (label-based) of CC and LC methods which are reported in Appendix table 9 and 10. E. Murphy-Hill, A. P. Black, An interactive ambient visualization for code for code clone detection, in: Proceedings of the 31st IEEE/ACM International International Conference on, Vol. 268–278. This disparity will confuse the ML algorithms. International Journal of Data Warehousing and Mining (IJDWM) 3 (3) (2007) De Lucia, Results: The analyses performed show that God Class, Long Method, Functional Decomposition, and Spaghetti Code have been heavily considered in the literature. In this section, we discuss how the existing studies differ from the proposed study. (1999)'s Code Bad Smells: Data Clumps, Switch Statements, Speculative Generality, Message Chains, and Middle Man, from Java Source … engineering (WCRE), 2012 19th working conference on, IEEE, 2012, pp. Initially, each data set have 420 instances. 0 Abstract: Code smells are structures in the source code that suggest the possibility of refactorings. In this paper, we propose a data-driven (i.e., Benchmark-based) method to derive threshold values for code metrics, which can be used for implementing detection rules for code smells. quality, in: ACM Sigplan Notices, Vol. Till now, in the literature azeem2019machine. Usually the detection techniques are based on the computation of different kinds of metrics, and other aspects related to the domain of the system under analysis, its size and other design features are not taken into account. dataset. (just 4 out of the total 30), and is not publicly available. 336–345. Dividing this measure by number of labels in dataset, results in a dimensionless measure known as density. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. The code smell detection tools proposed in the literature produce The remaining 37 systems can not detect code smells as they are not successfully compiled. There are two approaches that are widely used to handle the problems of MLC tsoumakas2007multi : problem transformation methods (PTM) and algorithm adoption methods (AAM). design change propagation probability matrix 1 (2007). Refactoring is a software engineering technique that, by applying a series of small behavior-preserving transformations, can improve a software system’s design, readability and extensibility. Existing approaches detected only one smell but, in the proposed one more than one smell can be detected. The author make no explicit reference to the applied datasets. Supervision comes in the form of previously labeled instances, from which an algorithm builds a model to automatically predict the labels of new instances. The performance of the proposed study is much better than the existing study. With this evidence, due to disparity, Di Nucci et al.di2018detecting got less performance on the concerned code smell datasets. G. Antoniol, E. Aïmeur, Support vector machines for anti-pattern fontana2016comparing , to simulate a more realistic scenario by merging the class and method-level wise datasets. Machine learning techniques help in addressing the issues … This project is a Java based detector, which can detect five of Fowler et al. ConcernMeBS Detector ConcernMeBS automatically detects code smells. 10/09/2020 ∙ by Min Fu, et al. 47–56. using association rule learning: A replicated study, in: Machine Learning In addition to it there are other measures added to multilabel dataset tsoumakas2007multi . Information and Software Technology. for the detection of code and design smells, in: Quality Software, 2009. Yang et al. Models based on a large set of independent variables have performed well. The authors configured the datasets of Fontana and provided new datasets which are suitable for real case scenario. Method: This paper presents a Systematic Literature Review (SLR) on Machine Learning Techniques for Code Smell Detection. R. Marinescu, Measurement and quality in object-oriented design, in: Software This approach can help software developrs to priortize or rank the classes or methods. This disparity will lead to forming the idea of multilable dataset. Animated Video created using Animaker - https://www.animaker.com An Atom Plugin to detect code smells in your Code di2018detecting , we have observed that there are 395 common instances in method level. Refactoring is the process of improving the quality of the code without altering its external behavior. In FE dataset has 715 instances among them 140 are positive, and 575 are negative. Reengineering (SANER), IEEE, 2018, pp. Similarly, in our code smell detection domain, instances are code elements and set of labels are code smells, i.e., a code element can contain more than one type of smell which is not addressed by the earlier approaches. ∙ smelly and non-smelly source code elements (classes or methods). share, To detect large-variance code clones (i.e. 34, ACM, 1999, pp. share. In addition, a boosting techniques is applied on 4 code smells viz., Data Class, Long Method, Feature Envy, God Class. That is, in this work, a multiclass can contains four class (00,01,10,11) values, 00 means not affected by both smells, 01 means affected by feature envy, 10 means affected by long method, and 11 means affected by both the smells.

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