2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)最新文献_第3页

An Empirical Study of Abbreviations and Expansions in Software Artifacts 软件构件中缩略语与展开的实证研究

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00040

Christian D. Newman, M. J. Decker, Reem S. Alsuhaibani, Anthony S Peruma, D. Kaushik, Emily Hill

{"title":"An Empirical Study of Abbreviations and Expansions in Software Artifacts","authors":"Christian D. Newman, M. J. Decker, Reem S. Alsuhaibani, Anthony S Peruma, D. Kaushik, Emily Hill","doi":"10.1109/ICSME.2019.00040","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00040","url":null,"abstract":"Expanding abbreviations is an important text normalization technique used for the purpose of either increasing developer comprehension or supporting the application of natural-language-based tools for source code identifiers. This paper closely studies abbreviations and where their expansions occur in different software artifacts. Without abbreviation expansion, developers will spend more time in comprehending the code they need to update, and tools analyzing software may obtain weak or non-generalizable results. There are numerous techniques for expanding abbreviations, most of which struggle to reach an average expansion accuracy of 59-62% on general source code identifiers. In this paper, we reveal some characteristics of abbreviations and their expansions through an empirical study of 861 abbreviation-expansion pairs extracted from 5 open-source systems in addition to analyzing previous literature. We use these characteristics to identify how current approaches may be complementary and how their results should be reported in the future to help maximize both our understanding of how they compare with other expansion techniques and their reproducibility.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122889541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

TECCD: A Tree Embedding Approach for Code Clone Detection 一种用于代码克隆检测的树嵌入方法

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00025

Yi Gao, Zan Wang, Shuang Liu, Lin Yang, Wei Sang, Yuanfang Cai

{"title":"TECCD: A Tree Embedding Approach for Code Clone Detection","authors":"Yi Gao, Zan Wang, Shuang Liu, Lin Yang, Wei Sang, Yuanfang Cai","doi":"10.1109/ICSME.2019.00025","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00025","url":null,"abstract":"Clone detection techniques have been explored for decades. Recently, deep learning techniques has been adopted to improve the code representation capability, and improve the state-of-the-art in code clone detection. These approaches usually require a transformation from AST to binary tree to incorporate syntactical information, which introduces overheads. Moreover, these approaches conduct term-embedding, which requires large training datasets. In this paper, we introduce a tree embedding technique to conduct clone detection. Our approach first conducts tree embedding to obtain a node vector for each intermediate node in the AST, which captures the structure information of ASTs. Then we compose a tree vector from its involving node vectors using a lightweight method. Lastly Euclidean distances between tree vectors are measured to determine code clones. We implement our approach in a tool called TECCD and conduct an evaluation using the BigCloneBench (BCB) and 7 other large scale Java projects. The results show that our approach achieves good accuracy and recall and outperforms existing approaches.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126167359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

Deep Learning Anti-Patterns from Code Metrics History 从代码度量历史中深度学习反模式

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00021

Antoine Barbez, Foutse Khomh, Yann-Gaël Guéhéneuc

{"title":"Deep Learning Anti-Patterns from Code Metrics History","authors":"Antoine Barbez, Foutse Khomh, Yann-Gaël Guéhéneuc","doi":"10.1109/ICSME.2019.00021","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00021","url":null,"abstract":"Anti-patterns are poor solutions to recurring design problems. Number of empirical studies have highlighted the negative impact of anti-patterns on software maintenance which motivated the development of various detection techniques. Most of these approaches rely on structural metrics of software systems to identify affected components while others exploit historical information by analyzing co-changes occurring between code components. By relying solely on one aspect of software systems (i.e., structural or historical), existing approaches miss some precious information which limits their performances. In this paper, we propose CAME (Convolutional Analysis of code Metrics Evolution), a deep-learning based approach that relies on both structural and historical information to detect anti-patterns. Our approach exploits historical values of structural code metrics mined from version control systems and uses a Convolutional Neural Network classifier to infer the presence of anti-patterns from this information. We experiment our approach for the widely know God Class anti-pattern and evaluate its performances on three software systems. With the results of our study, we show that: (1) using historical values of source code metrics allows to increase the precision; (2) CAME outperforms existing static machine-learning classifiers; and (3) CAME outperforms existing detection tools.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126716431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Continuous Collateral Privacy Risk Auditing of Evolving Autonomous Driving Software 不断发展的自动驾驶软件的连续附带隐私风险审计

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00055

Chang Liu, Krerkkiat Chusap, Zhongen Li, Zhaojie Chen, Dylan Rogers, Fanghao Song

{"title":"Continuous Collateral Privacy Risk Auditing of Evolving Autonomous Driving Software","authors":"Chang Liu, Krerkkiat Chusap, Zhongen Li, Zhaojie Chen, Dylan Rogers, Fanghao Song","doi":"10.1109/ICSME.2019.00055","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00055","url":null,"abstract":"Autonomous driving systems have a rich and diverse set of sensors and collect a tremendous amount of data during their operations. This has significant implications for individual privacy and induces a new type of potential privacy risks - collateral privacy risks. It is important for the public and the developer community to be aware of the collateral privacy risk posed by current autonomous driving software systems. We performed data privacy analysis for the Apollo project, an open-source autonomous driving software system. We applied source code-based privacy auditing techniques tailored for this particular problem and produced preliminary results, although there were unresolved open issues remaining. As we performed auditing, Apollo was upgraded from version 3.0 to 3.5 with significant under-the-hood technology changes. It was a challenge to perform the analysis as the underlying software evolves and maintain a result that is up-to-date. To address this challenge, we developed and deployed a continuous source code privacy risk analysis tool to assist in the process. In this paper, we discuss our experience and lessons learned from this industrial case study.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127447652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Modeling the Relationship Between Identifier Name and Behavior 标识符名称和行为之间的关系建模

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00062

Christian D. Newman, Anthony S Peruma, Reem S. Alsuhaibani

引用次数: 5

An Industrial Study on the Differences between Pre-Release and Post-Release Bugs 发布前和发布后bug差异的工业研究

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00019

Renaud Rwemalika, Marinos Kintis, Mike Papadakis, Yves Le Traon, Pierre Lorrach

{"title":"An Industrial Study on the Differences between Pre-Release and Post-Release Bugs","authors":"Renaud Rwemalika, Marinos Kintis, Mike Papadakis, Yves Le Traon, Pierre Lorrach","doi":"10.1109/ICSME.2019.00019","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00019","url":null,"abstract":"Software bugs constitute a frequent and common issue of software development. To deal with this problem, modern software development methodologies introduce dedicated quality assurance procedures. At the same time researchers aim at developing techniques capable of supporting the early discovery and fix of bugs. One important factor that guides such research attempts is the characteristics of software bugs and bug fixes. In this paper, we present an industrial study on the characteristics and differences between pre-release bugs, i.e. bugs detected during software development, and post-release bugs, i.e. bugs that escaped to production. Understanding such differences is of paramount importance as it will improve our understanding on the testing and debugging support that practitioners require from the research community, on the validity of the assumptions of several research techniques, and, most importantly, on the reasons why bugs escape to production. To this end, we analyze 37 industrial projects from BGL BNP Paribas and document the differences between pre-release bugs and post-release bugs. Our findings suggest that post-release bugs are more complex to fix, requiring developers to modify several source code files, written in different programming languages, and configuration files, as well. We also find that approximately 82% of the post-release bugs involve code additions and can be characterized as 'omission' bugs. Finally, we conclude the paper with a discussion on the implications of our study and provide guidance to future research directions.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132506088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Automated Identification of Over-Privileged SmartThings Apps 自动识别权限过大的智能事物应用程序

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00037

Atheer Abu Zaid, Manar H. Alalfi, A. Miri

引用次数: 2

Share, But be Aware: Security Smells in Python Gists 分享，但要警惕：Python Gists 中的安全隐患

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00087

Md. Rayhanur Rahman, A. Rahman, L. Williams

引用次数: 16

[Copyright notice] (版权)

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/icsme.2019.00003

引用次数: 0

Supporting Software Architecture Maintenance by Providing Task-Specific Recommendations 通过提供特定于任务的建议来支持软件架构维护

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) Pub Date : 2019-09-01 DOI: 10.1109/ICSME.2019.00060

M. Galster, Christoph Treude, Kelly Blincoe

引用次数: 0