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Comparison and evaluation of source code mining tools and techniques: A qualitative approach

INTELLIGENT DATA ANALYSIS 2013年第3期17卷 459-484页

作者： Khatoon, Shaheen Li, Guohui Mahmood, Azhar HUST Wuhan Hubei Peoples R China

Program source code substantially is structured and contains semantically rich programming constructs such as variables, functions, data structures, and program structures which indicate patterns. mining source code by using different data mining techniques to extract the valuable hidden patterns is the new revolution in software engineering. Over last decade many tools and techniques have been proposed by researcher to extract pertinent information and uncover relationships and trends from source code about a particular characteristic of Software Engineering (SE) tasks. These efforts have resulted in wide range of research body but currently there is no comprehensive overview exists. This paper surveys the tools and techniques which rely only on data mining methods to determine patterns from source code in context of programming, bug detection, maintenance, program understanding and software reuse. The work provides comparison and evaluation of the current state-of-the-art source code mining tools and techniques, and organizes the large amount of information into a coherent conceptual way. Thus the survey provides researchers with a concise overview of source code mining techniques and assists practitioners the selection of appropriate techniques for their work. The result of this review shows existing studies focus on one specific pattern being mined from source code such as special kind of bug detection. Thus, there is a need of multiple tools to test and find potential information from software which increase cost and time of development. Hence there is a strong need of tool which helps in developing quality software by automatically detecting different kind of bugs and generates relevant API code automatically to help in decreasing overall software development time.

关键词： source code mining data mining patterns programming rule copy-paste code bug detection API usage

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Android Botnet Detection An Integrated source code mining Approach 12

Android Botnet Detection An Integrated Source Code Mining Ap...

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12th International Conference on Internet Technology and Secured Transactions (ICITST)

作者： Alothman, Basil Rattadilok, Prapa De Montfort Univ Fac Technol Leicester LE1 9BH Leics England

ISBN: (纸本)9781908320933

Android is one of the most popular smartphone operating systems. This makes it one of the default targets for malicious cyber-attacks. Android's Play Store is not very restrictive which makes installing malicious apps easy. Botnets are amongst the most dangerous hacking approaches that are used nowadays on the internet. It is common for botnet developers to target smartphone users to install their malicious tools and target a larger number of devices. This is often done to gain access to sensitive data such as credit card details, or to cause damage to individual hosts or organisation resources by executing denial of service attacks. In this paper, we propose an approach to identify botnet Android mobile apps by means of source code mining. We analyse the source code via reverse engineering and data mining techniques for several examples of malicious and non-malicious apps. We use two approaches to build datasets. In the first, we perform text mining on the source code and construct several datasets and in the second we build one dataset by extracting source code metrics using an open-source tool. After building the datasets, we run several classification algorithms and assess their performance. Initial results show a high level of accuracy.

关键词： component Android Botnet Detection Document Analysis source code mining Android App Reverse Engineering Malware Detection

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KFCM algorithm based on the source code mining method study 5

KFCM algorithm based on the source code mining method study

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5th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Wang, Xun Hebei Software Inst Human Resources Dept Baoding City Peoples R China

ISBN: (纸本)9781479942626

This paper provides a algorithm, which is based on that kernelized fuzzy C-means uses on the study of source code mining, to solve the problem that the large number of quantities, multiple attributes and most of them discrete of software engineering. By using this algorithm, we can improve the efficiency of mining and seek faster and more effective cluster approaches. Meanwhile, we can also solve the problem that the KFCM algorithm can not cluster text data directly. Then we can over the defect of only being able to obtain the minimum values by integrating KFCM and genetic algorithm. Finally, the experiment shows that the improved KFCM algorithm has a good clustering performance and high efficiency on data mining.

关键词： C-means KFCMalgorithm source code mining

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Declarative Visitors to Ease Fine-grained source code mining with Full History on Billions of AST Nodes 13

Declarative Visitors to Ease Fine-grained Source Code Mining...

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12th International Conference on Generative Programming: Concepts and Experiences

作者： Dyer, Robert Rajan, Hridesh Nguyen, Tien N. Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9781450323734

Software repositories contain a vast wealth of information about software development. mining these repositories has proven useful for detecting patterns in software development, testing hypotheses for new software engineering approaches, etc. Specifically, mining source code has yielded significant insights into software development artifacts and processes. Unfortunately, mining source code at a large-scale remains a difficult task. Previous approaches had to either limit the scope of the projects studied, limit the scope of the mining task to be more coarse-grained, or sacrifice studying the history of the code due to both human and computational scalability issues. In this paper we address the substantial challenges of mining source code: a) at a very large scale;b) at a fine-grained level of detail;and c) with full history information. To address these challenges, we present domain-specific language features for source code mining. Our language features are inspired by object-oriented visitors and provide a default depth-first traversal strategy along with two expressions for defining custom traversals. We provide an implementation of these features in the Boa infrastructure for software repository mining and describe a code generation strategy into Java code. To show the usability of our domain-specific language features, we reproduced over 40 source code mining tasks from two large-scale previous studies in just 2 person-weeks. The resulting code for these tasks show between 2.0x-4.8x reduction in code size. Finally we perform a small controlled experiment to gain insights into how easily mining tasks written using our language features can be understood, with no prior training. We show a substantial number of tasks (77%) were understood by study participants, in about 3 minutes per task.

关键词： visitor pattern source code mining Boa

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A Study on Applying KFCM Algorithm to source code mining

A Study on Applying KFCM Algorithm to Source Code Mining

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2012 National Conference on Information Technology and Computer Science

作者： Liu Yonghui Computer and Information Engineering Dept.Baoding Vocational and Technical College Baoding,China Tian Jingjing Computer and Information Engineering Dept.Baoding Vocational and Technical College Baoding,China Zhang Lei Modern Education Dept.Baoding Vocational and Technical College Baoding,China

This paper provides a algorithm,which is based on that kernelized fuzzy C-means uses on the study of source code mining,to solve the problem that the large number of quantities,multiple attributes and most of them discrete of software *** using this algorithm,we can improve the efficiency of mining and seek faster and more effective cluster ***,we can also solve the problem that the KFCM algorithm can not cluster text data *** we can over the defect of only being able to obtain the minimum values by integrating KFCM and genetic ***,the experiment shows that the improved KFCM algorithm has a good clustering performance and high efficiency on data mining.

关键词： C-means KFCMalgorithm source code mining

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The Good, the Bad, and the Ugly: mining for Patterns in Student source code 3

The Good, the Bad, and the Ugly: Mining for Patterns in Stud...

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3rd International Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI)

作者： Mens, Kim Nijssen, Siegfried Hoang-Son Pham UCLouvain ICTEAM Inst Louvain La Neuve Belgium Hasselt Univ Data Sci Inst Hasselt Belgium

ISBN: (纸本)9781450386241

Research on source code mining has been explored to discover interesting structural regularities, API usage patterns, refactoring opportunities, bugs, crosscutting concerns, code clones and systematic changes. In this paper we present a pattern mining algorithm that uses frequent tree mining to mine for interesting good, bad or ugly coding idioms made by undergraduate students taking an introductory programming course. We do so by looking for patterns that distinguish positive examples, corresponding to the more correct answers to a question, from negative examples, corresponding to solutions that failed the question. We report promising initial results of this algorithm applied to the source code of over 500 students. Even though more work is needed to fine-tune and validate the algorithm further, we hope that it can lead to interesting insights that can eventually be integrated into an intelligent recommendation system to help students learn from their errors.

关键词： Pattern mining source code mining CS education programming

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Discovering Program Topoi via Hierarchical Agglomerative Clustering

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IEEE TRANSACTIONS ON RELIABILITY 2018年第3期67卷 758-770页

作者： Ieva, Carlo Gotlieb, Arnaud Kaci, Souhila Lazaar, Nadjib Simula Res Lab Dept Software Engn N-1364 Oslo Norway Univ Montpellier Lab Informat Robot & Microelect Montpellier F-34090 Montpellier France

In long lifespan software systems, specification documents can be outdated or even missing. Developing new software releases or checking whether some user requirements are still valid becomes challenging in this context. This challenge can be addressed by extracting high-level observable capabilities of a system by mining its source code and the available source-level documentation. This paper presents feature extraction and traceability (FEAT), an approach that automatically extracts topoi, which are summaries of the main capabilities of a program, given under the form of collections of code functions along with an index. FEAT acts in two steps: first, clustering: by mining the available source code, possibly augmented with code-level comments, hierarchical agglomerative clustering groups similar code functions. In addition, this process gathers an index for each function. Second, entry point selection: functions within a cluster are then ranked and presented to validation engineers as topoi candidates. We implemented FEAT on top of a general-purpose test management and optimization platform and performed an experimental study over 15 open-source software projects amounting to more than 1 M lines of codes proving that automatically discovering topoi is feasible and meaningful on realistic projects.

关键词： Clustering program analysis program topos software maintenance source code mining

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A Defect Estimator for source code: Linking Defect Reports with Programming Constructs Usage Metrics

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2020年第2期29卷 1–35页

作者： Kapur, Ritu Sodhi, Balwinder Indian Inst Technol Ropar Dept Comp Sci & Engn Rupnagar 140001 Punjab India

An important issue faced during software development is to identify defects and the properties of those defects, if found, in a given source file. Determining defectiveness of source code assumes significance due to its implications on software development and maintenance cost. We present a novel system to estimate the presence of defects in source code and detect attributes of the possible defects, such as the severity of defects. The salient elements of our system are: (i) a dataset of newly introduced source code metrics, called PROgramming CONstruct (PROCON) metrics, and (ii) a novel MachineLearning (ML)-based system, called Defect Estimator for source code (DESCo), that makes use of PROCON dataset for predicting defectiveness in a given scenario. The dataset was created by processing 30,400+ source files written in four popular programming languages, viz., C, C++, Java, and Python. The results of our experiments show that DESCo system outperforms one of the state-of-the-art methods with an improvement of 44.9%. To verify the correctness of our system, we compared the performance of 12 different ML algorithms with 50+ different combinations of their key parameters. Our system achieves the best results with SVM technique with a mean accuracy measure of 80.8%.

关键词： Maintaining software source code mining software defect prediction software metrics software faults and failures automated software engineering Al in software engineering

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APIUaaS: a reference architecture for facilitating API usage from a data analytics perspective

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IET SOFTWARE 2019年第5期13卷 466-478页

作者： Zhao, Jitong Liu, Yan Tongji Univ Sch Software Engn Caoan Rd 4800 Shanghai Peoples R China

source code examples are key resources for software developers to learn application programming interfaces (APIs) and to understand corresponding usage patterns. Developers usually have to utilise, evaluate and understand code examples from multiple sources, which involve heavy manually processing efforts. To reduce such efforts, there has been growing interest in developing source code mining and recommendation systems. This study proposes API usage as a service (APIUaaS), a reference architecture for facilitating API usage, which allows infrastructures to be built for recommending proper API code examples based on semi-automatic data analytics. This reference architecture contains five logical layers and six global-level architectural concerns. API queries are accepted from programmers, and corresponding code example candidates are extracted from the data sources layer. The detailed structural links between API elements and source codes are captured and stored in the data model & code assets layer. During the recommendation phase, API usages mining, clustering and ranking algorithms are enabled in the knowledge discover & intelligent model layer. Services such as code assist and bug detection are assembled in the API usage services layer. Finally, the authors evaluate APIUaaS from three perspectives: rationality, feasibility, and usability.

关键词： data analysis data mining application program interfaces source code (software) recommender systems cloud computing pattern clustering software architecture reference architecture data analytics perspective source code examples software developers application programming interfaces multiple sources source code mining recommendation systems APIUaaS semiautomatic data analytics global-level architectural concerns API queries data sources layer API elements data model & code assets layer ranking algorithms API usage services layer usage patterns API usage as a service API code examples code example candidates recommendation phase API usages mining clustering algorithm ranking algorithm knowledge discover & intelligent model layer

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WSDL term tokenization methods for IR-style Web services discovery

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SCIENCE OF COMPUTER PROGRAMMING 2012年第3期77卷 355-374页

作者： Wu, Chen Univ Western Australia ICRAR Nedlands WA 6009 Australia

The IR-style Web services discovery represents an important approach that applies proven techniques developed in the field of Information Retrieval (IR). Many studies exploited the Web Services Description Language (WSDL) syntax to extract useful service metadata for building indexes. However, a fundamental issue associated with this approach is the WSDL term tokenization. This paper proposes the application of three statistical methods for WSDL term tokenization-MDL, TP, and PPM. With the increasing need for effective IR-style Web services discovery facilities, term tokenization is of fundamental importance for properly indexing WSDL documents. We compare our applied methods with two baseline methods. The experiment suggests the superiority of MDL and PPM methods based on IR evaluation metrics. To the best of our knowledge, our work is the first to systematically investigate the issue of WSDL term tokenization for Web services discovery. Our solution can benefit source coding mining, in which a key step is to tokenize names (i.e. terms) of variables, functions, classes, modules, etc. for semantic analysis. Our methods could also be used for solving Web-related string tokenization problems such as URL analysis and Web scripts comprehension. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Web services discovery Information Retrieval String tokenization source code mining Data engineering

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