Code Smell Detection Tools Research Articles

Code smell detection by deep direct-learning and transfer-learning

Context:An excessive number of code smells make a software system hard to evolve and maintain. Machine learning methods, in addition to metric-based and heuristic-based methods, have been recently applied to detect code smells; however, current methods are considered far from mature. Objective:First, explore the feasibility of applying deep learning models to detect smells without extensive feature engineering. Second, investigate the possibility of applying transfer-learning in the context of detecting code smells. Methods:We train smell detection models based on Convolution Neural Networks and Recurrent Neural Networks as their principal hidden layers along with autoencoder models. For the first objective, we perform training and evaluation on C# samples, whereas for the second objective, we train the models from C# code and evaluate the models over Java code samples and vice-versa. Results:We find it feasible to detect smells using deep learning methods though the models’ performance is smell-specific. Our experiments show that transfer-learning is definitely feasible for implementation smells with performance comparable to that of direct-learning. This work opens up a new paradigm to detect code smells by transfer-learning especially for the programming languages where the comprehensive code smell detection tools are not available.

Code smells and refactoring: A tertiary systematic review of challenges and observations

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.

Code smell detection using multi-label classification approach

Code smells are characteristics of the software that indicates a code or design problem which can make software hard to understand, evolve, and maintain. There are several code smell detection tools proposed in the literature, but they produce different results. This is because smells are informally defined or subjective in nature. Machine learning techniques help in addressing the issues of subjectivity, which can learn and distinguish the characteristics of smelly and non-smelly source code elements (classes or methods). However, the existing machine learning techniques can only detect a single type of smell in the code element that does not correspond to a real-world scenario as a single element can have multiple design problems (smells). Further, the mechanisms proposed in the literature could not detect code smells by considering the correlation (co-occurrence) among them. To address these shortcomings, we propose and investigate the use of multi-label classification (MLC) methods to detect whether the given code element is affected by multiple smells or not. In this proposal, two code smell datasets available in the literature are converted into a multi-label dataset (MLD). In the MLD, we found that there is a positive correlation between the two smells (long method and feature envy). In the classification phase, the two methods of MLC considered the correlation among the smells and enhanced the performance (on average more than 95% accuracy) for the 10-fold cross-validation with the ten iterations. The findings reported help the researchers and developers in prioritizing the critical code elements for refactoring based on the number of code smells detected.

Towards a Reliable Identification of Deficient Code with a Combination of Software Metrics

Different challenges arise while detecting deficient software source code. Usually a large number of potentially problematic entities are identified when an individual software metric or individual quality aspect is used for the identification of deficient program entities. Additionally, a lot of these entities quite often turn out to be false positives, i.e., the metrics indicate poor quality whereas experienced developers do not consider program entities as problematic. The number of entities identified as potentially deficient does not decrease significantly when the identification of deficient entities is carried out by applying code smell detection rules. Moreover, the intersection of entities identified as allegedly deficient among different code smell detection tools is small, which suggests that the implementation of code smell detection rules are not consistent and uniform. To address these challenges, we present a novel approach for identifying deficient entities that is based on applying the majority function on the combination of software metrics. Program entities are assessed according to selected quality aspects that are evaluated with a set of software metrics and corresponding threshold values derived from benchmark data, considering the statistical distributions of software metrics values. The proposed approach was implemented and validated on projects developed in Java, C++ and C#. The validation of the proposed approach was done with expert judgment, where software developers and architects with multiple years of experiences assessed the quality of the software classes. Using a combination of software metrics as the criteria for the identification of deficient source code, the number of potentially deficient object-oriented program entities proved to be reduced. The results show the correctness of quality ratings determined by the proposed identification approach, and most importantly, confirm the absence of false positive entities.

Design of testing framework for code smell detection (OOPS) using BFO algorithm

Detection of bad smells refers to any indication in the program code of a execution that perhaps designate a issue, maintain the software and software evolution. Code Smell detection is a main challenging for software developers and their informal classification direct to the designing of various smell detection methods and software tools. It appraises 4 code smell detection tool in software like as a in Fusion, JDeodorant, PMD and Jspirit. In this research proposes a method for detection the bad code smells in software is called as code smell. Bad smell detection in software, OOSMs are used to identify the Source Code whereby Plug-in were implemented for code detection in which position of program initial code the bad smell appeared so that software refactoring can then acquire position. Classified the code smell, as a type of codes: long method, PIH, LPL, LC, SS and GOD class etc. Detection of the code smell and as a result applying the correct detection phases when require is significant to enhance the Quality of the code or program. The various tool has been proposed for detection of the code smell each one featured by particular properties. The main objective of this research work described our proposed method on using various tools for code smell detection. We find the major differences between them and dissimilar consequences we attained. The major drawback of current research work is that it focuses on one particular language which makes them restricted to one kind of programs only. These tools fail to detect the smelly code if any kind of change in environment is encountered. The base paper compares the most popular code smell detection tools on basis of various factors like accuracy, False Positive Rate etc. which gives a clear picture of functionality these tools possess. In this paper, a unique technique is designed to identify CSs. For this purpose, various object-oriented programming (OOPs)-based-metrics with their maintainability index are used. Further, code refactoring and optimization technique are applied to obtain low maintainability Index. Finally, the proposed scheme is evaluated to achieve satisfactory results. The results of the BFOA test defined that the lazy class caused framework defects in DLS, DR, and SE. However, the LPL caused no framework defects what so ever. The consequences of the connection rules test searched that the LCCS (Lazy Class Code Smell) caused structured defects in DE and DLS, which corresponded to the consequences of the BFOA test. In this research work, a proposed method is designed to verify the code smell. For this purpose, different OOPs based Software Metrics with their MI (Maintainability Index) are utilized. Further Code refactoring and optimization method id applied to attained the less maintainability index and evaluated to achieved satisfactory results.

On the evaluation of code smells and detection tools

Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Detection of code smells is challenging for developers and their informal definition leads to the implementation of multiple detection techniques and tools. This paper evaluates and compares four code smell detection tools, namely inFusion, JDeodorant, PMD, and JSpIRIT. 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. We calculated the accuracy of each tool in the detection of three code smells: God Class, God Method, and Feature Envy. Agreement was calculated among tools and between pairs of tools. One of our main findings is that the evaluated tools present different levels of accuracy in different contexts. For MobileMedia, for instance, the average recall varies from 0 to 58% and the average precision from 0 to 100%, while for Health Watcher the variations are 0 to 100% and 0 to 85%, respectively. Regarding the agreement, we found that the overall agreement between tools varies from 83 to 98% among all tools and from 67 to 100% between pairs of tools. We also conducted a secondary study of the evolution of code smells in both target systems and found that, in general, code smells are present from the moment of creation of a class or method in 74.4% of the cases of MobileMedia and 87.5% of Health Watcher.

Improving Smell Prediction: Developing an Improved Model with Supervised Learning Techniques

Objectives: To build a model for the prediction of the code smells using the supervised learning techniques. The motive to build code smell prediction model is to propose a model with less false positive code smells. Also, the proposed model is cross validated using 10-fold cross validation Methods/Statistical Analysis: To build a smell prediction model, two code smell detection tools are used i.e. IPlasma and PMD. The metrics are extracted using understand and IPlasma. To achieve the above mentioned objective, two experiments are performed. One is using the code smells of the PMD and the other one is using the smells of the IPLasma. The smells of the PMD are associated with the metrics that are extracted using Understand. Then, the model is trained using the different supervised learning algorithms that are called classifiers i.e. Random Forest, Naive Bayes and Kstar. Findings: In this research work, two experiments are performed. One is using the code smells of the PMD and other one is using the code smells of the IPlasma. From the results obtained i.e. with the code smells of the PMD, it is concluded that Random Forest predicts small number of false positive and false negative code smells as the precision and Recall of the Random Forest in each dataset is larger than the other two classifier’s. Moreover, the ROC value of Random Forest is higher in some datasets and in some datasets the ROC value of KStar is higher. The results obtained i.e. with the code smells of the IPlasma, it is concluded that again Random Forest predict code smells more correctly than the other two classifiers and give less number of false positive and false negative code smells. Moreover, there is an exception, there is one dataset in which Random Forest and KStar both shows 100% accuracy i.e., Precison and Recall both are equal to 1, which shows that both classifiers predicts no false positive and false negative code smells. Moreover, the ROC value of Random Forest is higher than the other two classifiers, even in some datasets it is equals to 1. Using this it is concluded that Random Forest gives the best code smell predicting model. Application/Improvements: The results of this experiments shows only one case where the false positive and false negative code smells are not predicted by the models. This can improve such that on apply each and every dataset it gives zero false positive code smells.

Dynamic and Automatic Feedback-Based Threshold Adaptation for Code Smell Detection

Most code smell detection tools expose thresholds to engineers for customization because code smell detection is essentially subjective and application specific. Another reason why engineers should customize these thresholds is that they have different working schedules and different requirements on software quality. They have their own unique need on precision and recall in smell detection. This unique need should be fulfilled by adjusting thresholds of smell detection tools. However, it is difficult for software engineers, especially inexperienced ones, to adjust often contradicting and related thresholds manually. One of the possible reasons is that engineers do not know the exact quantitative relation between threshold values and performance, e.g., precision. In this paper, we propose an approach to adapting thresholds automatically and dynamically. Engineers set a target precision manually according to their working schedules and quality requirements. With feedback from engineers, the proposed approach then automatically searches for a threshold setting to maximize recall while having precision close to the target precision. The proposed approach has been evaluated on open-source applications. Evaluation results suggest that the proposed approach is effective.

A review of code smell mining techniques

Over the past 15years, researchers presented numerous techniques and tools for mining code smells. It is imperative to classify, compare, and evaluate existing techniques and tools used for the detection of code smells because of their varying features and outcomes. This paper presents an up-to-date review on the state-of-the-art techniques and tools used for mining code smells from the source code of different software applications. We classify selected code smell detection techniques and tools based on their detection methods and analyze the results of the selected techniques. We present our observations and recommendations after our critical analysis of existing code smell techniques and tools. Our recommendations may be used by existing and new tool developers working in the field of code smell detection. The scope of this review is limited to research publications in the area of code smells that focus on detection of code smells as compared with previous reviews that cover all aspects of code smells. Copyright © 2015 John Wiley & Sons, Ltd.

Comparing and experimenting machine learning techniques for code smell detection

Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.

A Comparative Study on Code Smell Detection Tools

Refactoring is a technique to make a computer program more readable and maintainable. A bad smell is an indication of some setback in the code, which requires refactoring to deal with. Many tools are available for detection and removal of these code smells. These tools vary greatly in detection methodologies and acquire different competencies. In this work, we studied different code smell detection tools minutely and try to comprehend our analysis by forming a comparative of their features and working scenario. We also extracted some suggestions on the bases of variations found in the results of both detection tools.