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Application of SMOTE and LSSVM with Various Kernels for Predicting Refactoring at Method Level 1

25th International Conference on Neural Information Processing (ICONIP)

作者： Kumar, Lov Satapathy, Shashank Mouli Krishna, Aneesh BITS Pilani Hyderabad Dept Comp Sci & Informat Syst Hyderabad India Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Curtin Univ Sch Elect Engn Comp & Math Sci Bentley WA Australia

ISBN: (数字)9783030042219

ISBN: (纸本)9783030042219;9783030042202

Improving maintainability by refactoring is essentially being considered as one of the important aspect of software development. For large and complex systems, identification of code segments, which require re-factorization is a compelling task for software developers. Development of recommendation systems for suggesting methods, which require refactoring are achieved using this research work. Materials and Methods: Literature works considered source code metrics for object-oriented software systems in order to measure the complexity of a software. In order to predict the need of refactoring, the proposed system computes twenty-five different source code metrics at the method level and utilize them as features in a machine learning framework. An open source dataset consisting of five different software systems is being considered for conducting a series of experiments in order to assess the performance of proposed approach. LSSVM with SMOTE data imbalance technique are being utilized in order to overcome the class imbalance problem. Conclusion: Analysis of the results reveals that LS-SVM with RBF kernel using SMOTE results in the best performance.

关键词： LSSVM Kernels Refactoring source code metrics Smote

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An Empirical Analysis on Web Service Anti-Pattern Detection using a Machine Learning Framework 42

An Empirical Analysis on Web Service Anti-Pattern Detection ...

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42nd Annual IEEE-Computer-Society Computers, Software and Applications (COMPSAC) Conference - Staying Smarter in a Smartening World

作者： Kumar, Lov Sureka, Ashish BITS Pilani Hyderabad Telangana India Ashoka Univ Sonipat India

ISBN: (纸本)9781538626672

Web Services are application components characterised by interoperability, extensibility, distributed application development and service oriented architecture. A complex distributed application can be developed by combing several third-party web services. Anti-patterns are counter-productive and poor design and practices. Web services suffer from a multitude of anti-patterns such as God object Web service and Fine grained Web service. Our work is motivated by the need to build techniques for automatically detecting common web-services anti-patterns by static analysis of the source code implementing a web service. Our approach is based on the premise that summary values of object oriented source code metrics computed at a web service level can be used as a predictor for anti-patterns. We present an empirical analysis of 4 data sampling techniques to encounter the class imbalance problem, 5 feature ranking techniques to identify the most informative and relevant features and 8 machine learning algorithms for predicting 5 different types of anti-patterns on 226 real-world web services across several domains. We conclude that it is possible to predict anti-patterns using source code metrics and a machine learning framework. Our analysis reveals that the best performing classification algorithm is Random Forest, best performing data sampling technique is SMOTE and the best performing feature ranking method is OneR.

关键词： Anti-Patterns Empirical Software Engineering Feature Selection Imbalanced Learning Machine Learning Predictive Modeling Service Oriented Architecture Software Analytics source code Analysis source code metrics Web Services

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A Study on Low Complexity Models to Predict Flaws in the Linux source code 12

A Study on Low Complexity Models to Predict Flaws in the Lin...

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12th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Kanashiro, Lucas Ribeiro, Athos Silva, David Meirelles, Paulo Terceiro, Antonio Univ Sao Paulo FLOSS Competence Ctr Sao Paulo Brazil Univ Brasilia UnB Fac Gama Brasilia DF Brazil Linaro Ltd QA Serv Team Curitiba Parana Brazil

ISBN: (纸本)9781509050475

Due to the constant evolution of technology, each day brings new programming languages, development paradigms, and ways of evaluating processes. This is no different with source code metrics, where there is always new metric classes. To use a software metric to support decisions, it is necessary to understand how to perform the metric collection, calculation, interpretation, and analysis. The tasks of collecting and calculating source code metrics are most often automated, but how should we monitor them during the software development cycle? Our research aims to assist the software engineer to monitor metrics of vulnerability threats present in the source code through a reference prediction model, considering that real world software have non-functional security requirements, which implies the need to know how to monitor these requirements during the software development cycle. As a first result, this paper presents an empirical study on the evolution of the Linux project. Based on static analysis data, we propose low complexity models to study flaws in the Linux source code. About 391 versions of the project were analyzed by mining the official Linux repository using an approach that can be reproduced to perform similar studies. Our results show that it is possible to predict the number of warnings triggered by a static analyzer for a given software project revision as long as the software is continuously monitored.

关键词： source code Static Analysis source code metrics Common Weakness Enumeration Prediction Linux

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Application of LSSVM and SMOTE on Seven Open source Projects for Predicting Refactoring at Class Level 24

Application of LSSVM and SMOTE on Seven Open Source Projects...

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24th Asia-Pacific Software Engineering Conference (APSEC)

作者： Kumar, Lov Sureka, Ashish Thapar Univ Patiala Punjab India Ashoka Univ Rai Haryana India

ISBN: (纸本)9781538636817

source code refactoring consisting of modifying the structure of the source code without changing its functionality and external behavior. We present a method to predict refactoring candidates at class level which can help developers in improving their design and structure of source code while preserving the behavior. We propose a technique to predict refactoring candidates based on the application of a machine learning based framework. We use Least Squares Support Vector Machines (LS-SVM) as the learning algorithm, Principal Component Analysis (PCA) as a feature extraction technique and Synthetic Minority Over-sampling Technique (SMOTE) as a technique for handling imbalanced data. We start with 102 source code metrics as input features which are then reduced to 31 features after removing irrelevant and redundant features through statistical tests. We conduct a series of experiments on publicly available software engineering dataset consisting of seven open-source software systems in which the refactored classes are manually validated. We apply LS-SVM with three different functions: linear, polynomial and Radial Basis Function (RBF). Statistical significance test demonstrate that RBF kernel outperforms linear and polynomial kernel but there is no statistically significant difference between the performance of linear and polynomial kernel. Statistical significance test reveals that with-SMOTE technique outperforms without-SMOTE and all metrics outperforms PCA based metrics. The mean value of Area Under Curve (AUC) for LS-SVM RBF kernel is 0.96.

关键词： Empirical Software Engineering Machine Learning Predictive Modeling Refactoring Software Maintenance source code metrics

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An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes

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COMPUTER STANDARDS & INTERFACES 2017年 53卷 1-32页

作者： Kumar, Lov Misra, Sanjay Rath, Santanu Ku. Natl Inst Technol Dept CSE Rourkela India Atilim Univ Dept Comp Engn Ankara Turkey

Software fault prediction models are used to predict faulty modules at the very early stage of software development life cycle. Predicting fault proneness using source code metrics is an area that has attracted several researchers' attention. The performance of a model to assess fault proneness depends on the source code metrics which are considered as the input for the model. In this work, we have proposed a framework to validate the source code metrics and identify a suitable set of source code metrics with the aim to reduce irrelevant features and improve the performance of the fault prediction model. Initially, we applied a t-test analysis and univariate logistic regression analysis to each source code metric to evaluate their potential for predicting fault proneness. Next, we performed a correlation analysis and multivariate linear regression stepwise forward selection to find the right set of source code metrics for fault prediction. The obtained set of source code metrics are considered as the input to develop a fault prediction model using a neural network with five different training algorithms and three different ensemble methods. The effectiveness of the developed fault prediction models are evaluated using a proposed cost evaluation framework. We performed experiments on fifty six Open source Java projects. The experimental results reveal that the model developed by considering the selected set of source code metrics using the suggested source code metrics validation framework as the input achieves better results compared to all other metrics. The experimental results also demonstrate that the fault prediction model is best suitable for projects with faulty classes less than the threshold value depending on fault identification efficiency (low - 48.89%, median- 39.26%, and high - 27.86%).

关键词： Feature selection techniques Artificial neural network Ensemble method source code metrics Cost analysis framework

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Software maintainability prediction using hybrid neural network and fuzzy logic approach with parallel computing concept

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2017年第2期8卷 1487-1502页

作者： Kumar, Lov Rath, Santanu Ku Natl Inst Technol Dept Comp Sci & Engn Rourkela India

In present day scenario, majority of software companies use object-oriented concept to develop software systems as it enables effective design, development, testing and maintenance, in addition to the optimal characterization of the software system. With the increase in number of these software systems, their effective maintenance aspect becomes very important day by day. In this study, Neuro-Fuzzy approach: hybrid neural network and fuzzy logic approach has been considered to develop a maintainability model using ten different object-oriented static source code metrics as input. This method is applied on maintainability data of two commercial software products such as UIMS and QUES. Rough set analysis (RSA) and principal component analysis (PCA) are used to select suitable set of metrics from the ten metrics employed to improve performance of maintainability prediction model. From experimental results, it is observed that Neuro-Fuzzy model can effectively predict the maintainability of object-oriented software systems. After implementing parallel computing concept, it is observed that the training time gets reduced to a significant amount when the number of computing nodes were increased. Further it is observed that selected subset of metrics using feature selection techniques i.e., PCA, and RSA was able to predict maintainability with higher accuracy.

关键词： Artificial neural network Fuzzy Logic source code metrics Maintainability Parallel computing

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Investigating the Prioritization of Unit Testing Effort using Software metrics 12

Investigating the Prioritization of Unit Testing Effort usin...

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12th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE)

作者： Toure, Fadel Badri, Mourad Lamontagne, Luc Univ Quebec Trois Rivieres Dept Math & Comp Sci Trois Rivieres PQ Canada Univ Laval Dept Software Engn & Comp Sci Quebec City PQ Canada

ISBN: (纸本)9789897582509

In object-oriented software, unit testing is a level of software testing where each individual class is tested by a dedicated unit test class. Unfortunately, due to time and resources constraints, this phase does not cover all classes. The testing efforts are often focused on particular classes. In this paper, we investigate an approach based on software information history to support the prioritization of classes to be tested. To achieve this goal, we first analyzed different attributes of ten open-source Java software systems for which JUnit test cases have been developed for several classes. We used the mean and the logistic regression analysis to characterize the classes for which JUnit test classes have been developed by testers. Second, we used two classifiers trained on metrics values and unit tests information collected from the selected systems. The classifiers provide, for each software, a set of classes on which unit testing efforts have to be focused. The obtained sets have been compared to the sets of classes for which JUnit test classes have been developed by testers. Results show that: (1) the metrics average values of tested classes are significantly different from the metrics average values of other classes, (2) there is a significant relationship between the fact that a JUnit test class has been developed for a class and its attributes, and (3) the sets of classes suggested by classifiers reflect the testers' selection properly.

关键词： Tests Prioritization Unit Tests source code metrics Logistic Regression Machine Learning

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An Empirical Analysis on Effective Fault Prediction Model Developed using Ensemble Methods 41

An Empirical Analysis on Effective Fault Prediction Model De...

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41st IEEE Annual Computer Software and Applications Conference (COMPSAC)

作者： Kumar, Lov Rath, Santanu Sureka, Ashish NIT Rourkela Rourkela India ABB Corp Res Bangalore Karnataka India

ISBN: (纸本)9781538603673

Software fault prediction models are employed to optimize testing resource allocation by identifying fault-prone classes before testing phases. We apply three different ensemble methods to develop a model for predicting fault proneness. We propose a framework to validate the source code metrics and select the right set of metrics with the objective to improve the performance of the fault prediction model. The fault prediction models are then validated using a cost evaluation framework. We conduct a series of experiments on 45 open source project dataset. Key conclusions from our experiments are: (1) Majority Voting Ensemble (MVE) methods outperformed other methods (2) selected set of source code metrics using the suggested source code metrics using validation framework as the input achieves better results compared to all other metrics (3) fault prediction method is effective for software projects with a percentage of faulty classes lower than the threshold value (low - 54.82%, medium - 41.04%, high - 28.10%)

关键词： Software Fault Prediction Machine Learning Predictive Modeling source code metrics Ensemble Methods

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Neural Network with Multiple Training Methods for Web Service Quality of Service Parameter Prediction 10

Neural Network with Multiple Training Methods for Web Servic...

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10th International Conference on Contemporary Computing (IC3)

作者： Kumar, Lov Sureka, Ashish NIT Rourkela Rourkela India ABB Corp Res Bangalore Karnataka India

ISBN: (纸本)9781538630778

Web services have several Quality of Service (QoS) properties. Some of the QoS parameters for web services are availability, response time, throughput, modularity, reliability, and interoperability. A client of a web service can have several web services with similar functionality but different QoS properties for application integration. QoS properties play a decisive factor in selecting the best web services from amongst services having similar functionality. Often QoS parameters are not available, not easy to compute or outdated. We present a method to estimate the QoS parameters of web services from the information contained in web service interfaces. We propose a method based on extracting several data, procedural and structural quantity metrics from the web service interfaces and using them as predictors for estimating the QoS properties. We apply neural network method with 6 different training methods for building a predictive model. Our results demonstrate that the proposed approach is effective. Our experimental results reveal that the structural quality metrics outperforms the procedural and data quality metrics in-terms of the RMSE (Root-Mean-Square Error) performance metric. We conclude that the NLM method (Neural Network with Levenberg-Marquardt training method) out performs five other popular neural network training methods.

关键词： Software Engineering source code metrics Neural Networks Machine Learning Quality of Service (QoS) Web Services

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Unsupervised Learning Based Approach for Plagiarism Detection in Programming Assignments 17

Unsupervised Learning Based Approach for Plagiarism Detectio...

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10th Innovations in Software Engineering Conference (ISEC)

作者： Yasaswi, Jitendra Kailash, Sri Chilupuri, Anil Purini, Suresh Jawahar, C. V. IIIT Hyderabad Hyderabad India

ISBN: (纸本)9781450348560

In this work, we propose a novel hybrid approach for automatic plagiarism detection in programming assignments. Most of the well known plagiarism detectors either employ a text-based approach or use features based on the property of the program at a syntactic level. However, both these approaches succumb to code obfuscation which is a huge obstacle for automatic software plagiarism detection. Our proposed method uses static features extracted from the intermediate representation of a program in a compiler infrastructure such as gcc. We demonstrate the use of unsupervised learning techniques on the extracted feature representations and show that our system is robust to code obfuscation. We test our method on assignments from introductory programming course. The preliminary results show that our system is better when compared to other popular tools like MOSS. For visualizing the local and global structure of the features, we obtained the low-dimensional representations of our features using a popular technique called t-SNE, a variation of Stochastic Neighbor Embedding, which can preserve neighborhood identity in low-dimensions. Based on this idea of preserving neighborhood identity, we mine interesting information such as the diversity in student solution approaches to a given problem. The presence of well de fined clusters in low-dimensional visualizations demonstrate that our features are capable of capturing interesting programming patterns.

关键词： source code metrics Visualization code obfuscation Feature representations Neighborhood embedding Unsupervised learning Plagiarism

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