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检索条件"任意字段=IEEE Working Conference on Source Code Analysis and Manipulation"

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code Clone Tracer (CCT): A Tracking Tool for Analyzing Human and Social Factors in Creating and Reusing code Clones 20

Code Clone Tracer (CCT): A Tracking Tool for Analyzing Human...

引用

20th ieee/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2019)

作者： Kukita, Yusukc Noguchi, Kojiro Ohira, Masao Wakayama Univ Grad Sch Syst Engn Wakayama Japan

ISBN: (纸本)9781728116518

This paper introduces a tool called CCT (code Clone Tracer) which is designed to help practitioners and researchers analyze the impacts of human/social factors on software quality in creating and reusing code clones. CCT tracks a history of created and reused code clones for all the revisions in a distributed version control system such as Git. The tool also has features to analyze the relationship between bug-inducing changes and developers, working together with a bug tracking system such as Bugzilla and JIRA. This paper reports our pilot case study where CCT is applied to three open source projects (RxJava, c:geo, and Jackson databind). The results of the case study indicated that a small number of particular developers were involved in many of clone changes to resolve issues.

关键词： code clone tracking software evolution social network analysis software quality open source software development

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Proceedings - 7th ieee working conference on Software Visualization, VISSOFT 2019

Proceedings - 7th IEEE Working Conference on Software Visual...

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7th ieee working conference on Software Visualization, VISSOFT 2019

ISBN: (纸本)9781728149394

The proceedings contain 13 papers. The topics discussed include: CorpusVis - visualizing software metrics at scale;codeHouse: VR code visualization tool;CloneCompass: visualizations for exploring assembly code clone ecosystems;exploring eye tracking data on source code via dual space analysis;performance evolution matrix: visualizing performance variations along software versions;and a tertiary systematic literature review on software visualization.

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Challenges of Implementing Cross Translation Unit analysis in Clang Static Analyzer 18

Challenges of Implementing Cross Translation Unit Analysis i...

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18th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Horvath, Gabor Szecsi, Peter Gera, Zoltan Krupp, Daniel Pataki, Norbert Eotovos Lorand Univ Dept Programming Languages & Compilers Budapest Hungary Ericsson Ltd Budapest Hungary

ISBN: (纸本)9781538682906

Static analysis is a great approach to find bugs and code smells. Some of the errors span across multiple translation units. Unfortunately, separate compilation makes cross translation unit analysis challenging for C family languages. In this paper, we describe a model and an implementation for cross translation unit symbolic execution for C family languages. We were able to extend the scope of the analysis without modifying any of the existing checkers. The analysis is implemented in the open source Clang compiler. We also measured the performance of the approach and the quality of the reports. The solution proved to be scalable to large codebases and the number of findings increased significantly for the evaluated projects. The implementation is already accepted into mainline Clang [1].

关键词： static analysis symbolic execution cross translation unit Clang whole program analysis

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Annotation practices in Android apps

Annotation practices in Android apps

引用

ieee International Workshop on source code analysis and manipulation

作者： Ajay Kumar Jha Sarah Nadi University of Alberta Edmonton Canada

ISBN: (数字)9781728192482

ISBN: (纸本)9781728192499

Understanding the adoption and usage of any programming language feature is crucial for improving it. Existing studies indicate that Java annotations are widely used by developers. However, there is currently no empirical data on annotation usage in Android apps. Android apps are often smaller than general Java applications and typically use Android APIs or specific libraries catered to the mobile environment. Therefore, it is not clear if the results of existing Java studies hold for Android apps. In this paper, we investigate annotation practices in Android apps through an empirical study of 1,141 open-source apps. Using previously studied metrics, we first compare annotation usage in Android apps to existing results from general Java applications. Then, for the first time, we study why developers declare custom annotations. Our results show that the density of annotations and the values of various other annotation metrics are notably less in Android apps than in Java projects. Additionally, the types of annotations used in Android apps are different than those in Java, with many Android-specific annotations. These results imply that researchers may need to distinguish mobile apps while performing studies on programming language features. However, we also found examples of extreme usage of annotations with, for example, a large number of attributes, as well as a low adoption rate for most annotations. By looking at such results, annotation designers can assess adoption patterns and take various improvement measures, such as modularizing their offered annotations or cleaning up unused ones. Finally, we find that developers declare custom annotations in different apps but with the same purpose, which presents an opportunity for annotation designers to create new annotations.

关键词： Measurement Java Computer languages Annotations Cleaning Mobile applications Open source software

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Graal: The Quest for source code Knowledge 18

Graal: The Quest for Source Code Knowledge

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18th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Cosentino, Valerio Duenas, Santiago Zerouali, Ahmed Robles, Gregorio Gonzalez-Barahona, Jesus M. Bitergia Madrid Spain Univ Rey Juan Carlos Madrid Spain

ISBN: (纸本)9781538682906

source code analysis tools are designed to analyze code artifacts with different intents, which span from improving the quality and security of the software to easing refactoring and reverse engineering activities. However, most tools do not come with features to periodically schedule their analysis or to be executed on a battery of repositories, and lack support to combine their results with other analysis tools. Thus, researchers and practitioners are often forced to develop ad-hoc scripts to meet their needs. This comes at the risk of obtaining wrong results (because of the lack of testing) and of hindering replication by other research teams. In addition, the resulting scripts are often not meant to be customized nor designed for incrementality, scalability and extensibility. In this paper we present Graal, which empowers users with a customizable, scalable and incremental approach to conduct source code analysis and enables relating the obtained results with other software project data. Graal leverages on and extends the functionalities of GrimoireLab, a strong free software tool developed by Bitergia, a company devoted to offer commercial software development analytics, and part of the CHAOSS project of the Linux Foundation.

关键词： Software mining empirical software engineering open source software development software analytics

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Obfuscating Java Programs by Translating Selected Portions of Bytecode to Native Libraries 18

Obfuscating Java Programs by Translating Selected Portions o...

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18th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Pizzolotto, Davide Ceccato, Mariano Fdn Bruno Kessler Trento Italy Univ Trento Trento Italy

ISBN: (纸本)9781538682906

code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, high level bytecode still contains high level relevant information that an attacker might exploit. In order to enable more resilient obfuscations, part of these programs might be implemented with programming languages (e.g., C) that compile to low level machine-dependent code. In fact, machine code contains and leaks less high level information and it enables more resilient obfuscations. In this paper, we present an approach to automatically translate critical sections of high level Java bytecode to C code, so that more effective obfuscations can be resorted to. Moreover, a developer can still work with a single programming language, i.e., Java.

关键词： source code translation Program transformation code obfuscation

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SCC: Automatic Classification of code Snippets 18

SCC: Automatic Classification of Code Snippets

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18th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Alreshedy, Kamel Dharmaretnam, Dhanush German, Daniel M. Srinivasan, Venkatesh Gulliver, T. Aaron Univ Victoria Victoria BC V8W 2Y2 Canada

ISBN: (纸本)9781538682906

Determining the programming language of a source code file has been considered in the research community;it has been shown that Machine Learning (ML) and Natural Language Processing (NLP) algorithms can be effective in identifying the programming language of source code files. However, determining the programming language of a code snippet or a few lines of source code is still a challenging task. Online forums such as Stack Overflow and code repositories such as GitHub contain a large number of code snippets. In this paper, we describe source code Classification (SCC), a classifier that can identify the programming language of code snippets written in 21 different programming languages. A Multinomial Naive Bayes (MNB) classifier is employed which is trained using Stack Overflow posts. It is shown to achieve an accuracy of 75% which is higher than that with Programming Languages Identification (PLI-a proprietary online classifier of snippets) whose accuracy is only 55.5%. The average score for precision, recall and the F1 score with the proposed tool are 0.76, 0.75 and 0.75, respectively. In addition, it can distinguish between code snippets from a family of programming languages such as C, C++ and C#, and can also identify the programming language version such as C# 3.0, C# 4.0 and C# 5.0.

关键词： Classification Machine Learning Natural Language Processing Programming languages

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Semantics-Based code Search Using Input/Output Examples 18

Semantics-Based Code Search Using Input/Output Examples

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18th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Jiang, Renhe Chen, Zhengzhao Zhang, Zejun Pei, Yu Pan, Minxue Zhang, Tian Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781538682906

As the quality and quantity of open source code increase, semantics-based code search has become an emerging need for software developers to retrieve and reuse existing source code. We present an approach of semantics-based code search using input/output examples for the Java language. Our approach encodes Java methods in code repositories into path constraints via symbolic analysis and leverages SMT solvers to find the methods whose path constraints can satisfy the given input/output examples. Our approach extends the applicability of the semantics-based search technology to more general Java code compared with existing methods. To evaluate our approach, we encoded 1228 methods from GitHub and applied semantics-based code search on 35 queries extracted from Stack Overflow. Correct method code for 29 queries was obtained during the search and the average search time was just about 48 seconds.

关键词： code query symbolic analysis SMT solver

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DCT: An Scalable Multi-Objective Module Clustering Tool

DCT: An Scalable Multi-Objective Module Clustering Tool

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ieee International Workshop on source code analysis and manipulation

作者： Ana Paula M. Tarchetti Luís Amaral Marcos C. Oliveira Rodrigo Bonifácio Gustavo Pinto David Lo University of Brasília Brazilian Ministry of Economy Federal University of Para Singapore Management University

ISBN: (数字)9781728192482

ISBN: (纸本)9781728192499

Maintaining complex software systems is a time-consuming and challenging task. Practitioners must have a general understanding of the system's decomposition and how the system's developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 years, there are just a few tools publicly available. Still, these available tools do not scale to large scenarios, in particular, when optimizing multi-objectives. In this paper we present the Draco Clustering Tool (DCT), a new software module clustering tool. DCT design decisions make multi-objective software clusterization feasible, even for software systems comprising up to 1,000 modules. We report an empirical study that compares DCT with other available multi-objective tool (HD-NSGA-II), and both DCT and HD-NSGA-II with mono-objective tools (BUNCH and HD-LNS). We evidence that DCT solves the scalability issue when clustering medium size projects in a multi-objective mode. In a more extreme case, DCT was able to cluster Druid (an analytics data store) 221 times faster than HD-NSGA-II.

关键词： Scalability conferences Software algorithms Tools Software systems Discrete cosine transforms Task analysis

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On the Use of Machine Learning Techniques Towards the Design of Cloud Based Automatic code Clone Validation Tools 18

On the Use of Machine Learning Techniques Towards the Design...

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18th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Mostaeen, Golam Svajlenko, Jeffrey Roy, Banani Roy, Chanchal K. Schneider, Kevin Univ Saskatchewan Dept Comp Sci Saskatoon SK Canada

ISBN: (纸本)9781538682906

A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, a great many numbers of code clone detection techniques and tools have been proposed and studied over the last decade. To detect all possible similar source code patterns in general, the clone detection tools work on syntax level (such as texts, tokens, AST and so on) while lacking user-specific preferences. This often means the reported clones must be manually validated prior to any analysis in order to filter out the true positive clones from task or user-specific considerations. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning based approach for automating the validation process. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method shows promising results in several comparative studies with the existing related approaches for automatic code clone validation. We also present our experimental results in terms of different code clone detection tools, machine learning algorithms and open source software systems.

关键词： code clones validation Machine Learning Clone Management

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