2021 28th Asia-Pacific Software Engineering Conference (APSEC)最新文献_第3页

S2 LMMD: Cross-Project Software Defect Prediction via Statement Semantic Learning and Maximum Mean Discrepancy 基于语句语义学习和最大平均差异的跨项目软件缺陷预测

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00044

Wangshu Liu, Yongteng Zhu, Xiang Chen, Qing Gu, Xingya Wang, Shenkai Gu

{"title":"S2 LMMD: Cross-Project Software Defect Prediction via Statement Semantic Learning and Maximum Mean Discrepancy","authors":"Wangshu Liu, Yongteng Zhu, Xiang Chen, Qing Gu, Xingya Wang, Shenkai Gu","doi":"10.1109/APSEC53868.2021.00044","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00044","url":null,"abstract":"Different from within-project software defect prediction (WPDP), cross-project software defect prediction (CPDP) does not require sufficient training data and can help developers in the early stages of software development. Recent studies tried to learn semantic features for CPDP by feeding neural networks with abstract syntax tree (AST) token vectors. However, the ASTs directly parsed from software modules usually have complex structures, which are reflected on more nodes and deeper size, and the transfer learning is not regularly adopted to further reduce the data distribution difference between the source project and the target project. To solve these problems, we aim to joint learn the statement level trees (SLT) and alleviate data distribution difference with maximum mean discrepancy (MMD) to improve defect prediction performance on CPDP. Specifically, we propose a novel cross-project defect prediction method S2LMMD via statement semantic learning and MMD. We first construct the SLT by splitting the original AST on specified node. Then we generate more effective semantic features by learning of sequence embedding with Bi-GRU neural network. Finally, a transfer loss MMD is carried out to keep more common characteristics across different project datasets to further improve CPDP performance. To verify the effectiveness of our proposed method, we conducted experiments on ten widely used open-source projects and evaluated the experimental performance by using AUC measures. Our empirical results show that our proposed method S2LMMD can significantly outperform eight state-of-the-art baselines. In addition, for semantic learning, SLT has a higher influence on CPDP, while MMD is of great significance in transfer learning.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125891593","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

Design of Software Architecture for Neural Network Cooperation: Case of Forgery Detection 神经网络协同软件体系结构设计:以伪造检测为例

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00021

Akira Mizutani, Masami Noro, Atsushi Sawada

{"title":"Design of Software Architecture for Neural Network Cooperation: Case of Forgery Detection","authors":"Akira Mizutani, Masami Noro, Atsushi Sawada","doi":"10.1109/APSEC53868.2021.00021","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00021","url":null,"abstract":"Recent technological advances in media tampering has been the cause of many harmful forged images. Tampering detection methods became major research topics to cope with it in the neural network community. The methods almost always aim at detecting a specific forgery. That is, a general detecting method to find any tampering has not been invented so far. This paper concerns about a software architecture for organizing multiple neural networks to detect multiple kinds of forgeries. The key issue here is to construct, from the meta-level, a mechanism for an ensemble of front-end neural networks to select a neural network which makes a decision. Under this architecture, we implemented a prototype for detecting forged images resulted from multiple tampering methods of copy-move and compression. In order to demonstrate that our architecture works well, we examined a case study with a total of 120,000 patches which consist of three classes of copy-move, compression and untampered data, 40,000 patches for each. The result shows our proposed method successfully classified 108,954 out of 120,000 patches with 90.82 % accuracy. We also give discussions on our architectural implication to avoid concept drift. Our architecture is designed to be a context-oriented and meta-level, which has a two-layered structure: meta and base. The neural networks can be categorized into base-level components, whereas a component coordinating the networks is addressed in meta-level. The architecture explains that the concept drift can be handled in the meta-level. Through the discussions on the techniques of transfer learning, online learning, and ensemble learning in terms of the architecture we constructed, it is concluded that we could construct a universal architecture to coordinate machine learning components.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"127 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126273639","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: 0

Scalable Fault Detection Based on Precise Access Path 基于精确访问路径的可扩展故障检测

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00054

Chi Li, Yuexing Wang, Min Zhou, M. Gu

{"title":"Scalable Fault Detection Based on Precise Access Path","authors":"Chi Li, Yuexing Wang, Min Zhou, M. Gu","doi":"10.1109/APSEC53868.2021.00054","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00054","url":null,"abstract":"Precise static analysis is necessary for an industrial environment to ensure reliability and security, which is usually field-sensitive and inter-procedural. However, it faces the problem of insufficient scale capability when being applied to various industrial environments: (1) Field-sensitive analysis can not assure termination if field accesses are modeled by unbounded access paths; (2) Inter-procedural analysis may lead to path explosion problems because of the unbounded length of call chains. While using longer access paths or call chains can improve precision, the analysis may have poor performance in terms of efficiency. Specifically, an industry-strength method should be scalable enough to face different applications. This paper presents a scalable fault detection method based on the precise access path. Precise access path models a memory location with accurate operations and offsets from a source. Points-to relations of variables are used to refine it. It can differentiate elements of aggregate structures and is more precise than the ordinary access path. Based on the precise access path, we perform an inter-procedural analysis with the help of an intra-procedural analysis and combined function summary. Furthermore, our method is designed backward to detect error handling bugs. Compared with the state-of-the-art tools, our method is more scalable, with higher precision and efficiency on both benchmarks and 11 widely-used applications.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130058615","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: 0

An Empirical Study on the Usage and Evolution of Identifier Styles in Practice 实践中标识语风格使用与演变的实证研究

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00025

Jingxuan Zhang, W. Zou, Zhiqiu Huang

{"title":"An Empirical Study on the Usage and Evolution of Identifier Styles in Practice","authors":"Jingxuan Zhang, W. Zou, Zhiqiu Huang","doi":"10.1109/APSEC53868.2021.00025","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00025","url":null,"abstract":"Identifiers play an important role in helping developers comprehend and maintain source code. In practice, developers usually employ two widely-used identifier styles, i.e., snake case and camel case, to format identifiers to make them understandable and informative. Despite researchers have empirically investigated the impacts of identifier styles on code comprehension activities, the usage and evolution of identifier styles, however, have not been fully explored. How are individual identifier styles formed in practice? How would identifier styles change and evolve? What are the potential impacts of identifier style-changes? Questions like these are important but have not been fully answered yet. In this paper, we conducted an empirical study on 9,792 GitHub projects to gain some insights into these problems. Specifically, we first analyzed how different identifier styles were formed in real software projects. Next, we explored the change patterns of identifier styles along with the project evolution. Finally, we investigated the potential impacts as well as categories of identifier style-changes. Our empirical results achieved some interesting findings. For example, we first reported some identifier style-change patterns (e.g., snake case →camel case → snake case), which could help developers resolve style-change problems in practice. Our study also provided some hints for researchers and developers when they use specific identifier styles in programs. For example, when researchers explore the impacts of identifier styles on code comprehension, they are suggested to consider the imbalanced distribution phenomenon of individual identifier styles. Besides, it is worthwhile for developers to build an identifier style-change prediction and propagation tool to reduce the style-change costs.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127603336","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: 1

An Exploration Study On the Dependency Among Vulnerabilities and Bugs 漏洞与bug依赖关系的探索性研究

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00074

Shaikh Mostafa, Xiaoyin Wang

引用次数: 0

Verification Assisted Gas Reduction for Smart Contracts 智能合约的验证辅助减气

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00034

Bo Gao, Siyuan Shen, Ling Shi, Jiaying Li, Jun Sun, Lei Bu

{"title":"Verification Assisted Gas Reduction for Smart Contracts","authors":"Bo Gao, Siyuan Shen, Ling Shi, Jiaying Li, Jun Sun, Lei Bu","doi":"10.1109/APSEC53868.2021.00034","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00034","url":null,"abstract":"Smart contracts are computerized transaction protocols built on top of blockchain networks. Users are charged with fees, a.k.a. gas in Ethereum, when they create, deploy or execute smart contracts. Since smart contracts may contain vulnerabilities which may result in huge financial loss, developers and smart contract compilers often insert codes for security checks. The trouble is that those codes consume gas every time they are executed. Many of the inserted codes are however redundant. In this work, we present sOptimize, a tool that optimizes smart contract gas consumption automatically without compromising functionality or security. sOptimize works on smart contract bytecode, statically identifies 3 kinds of code patterns, and further removes them through verification-assisted techniques. The resulting code is guaranteed to be equivalent to the original one and can be directly deployed on blockchain. We evaluate sOptimize on a collection of 1,152 real-world smart contracts and show that it optimizes 43% of them, and the reduction on gas consumption is about 2.0% while in deployment and 1.2% in transactions, the amount can be as high as 954,201 gas units per contract.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"10 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114171386","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: 4

Effective Bug Triage Based on a Hybrid Neural Network 基于混合神经网络的有效Bug分类

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00016

Hongbing Wang, Qi Li

{"title":"Effective Bug Triage Based on a Hybrid Neural Network","authors":"Hongbing Wang, Qi Li","doi":"10.1109/APSEC53868.2021.00016","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00016","url":null,"abstract":"With the increasing scale and complexity of open source software, the quality of software has become a focus to which repairers pay close attention. Due to the inevitable existence of some known or unknown bugs in software,under certain conditions, software bugs may directly cause program running errors, and then produce abnormal running results and wrong program behavior, which will cause huge economic losses. Therefore, software defect repair is an important part of software evolution and quality assurance. Quickly and efficiently assigning defect reports to the right repairer for repair,to ensure efficiency and reduce the cost of open-source software development is an important problem that must be solved in software quality improvement. In this study, we propose a new defect report repair recommendation algorithm, RCNN, which can effectively learn the features of the defect report and recommend the appropriate repairer according to the feature. The proposed algorithm uses a CNN convolution kernel to capture the local information of the text and RNN is used to capture the sequence information of the text. The attention mechanism is introduced to learn the contribution ratio of each part of the text to the overall semantic information of the text. Thus, to a certain extent, it makes up for the defect that RNN cannot effectively learn and monitor remote information. Through experiments on the Eclipse and Mozilla datasets, compared with NB (naive Bayes), SVM (support vector machines), LeeCNN and DBRNNA, the RCNN model can effectively find the appropriate bug repairer among many repairers, and achieve higher classification accuracy.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114341946","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: 3

[Copyright notice] (版权)

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/apsec53868.2021.00003

引用次数: 0

Improving Semantic Consistency of Variable Names with Use-Flow Graph Analysis 用用流图分析改进变量名的语义一致性

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/APSEC53868.2021.00030

Yusuke Shinyama, Yoshitaka Arahori, K. Gondow

引用次数: 0

Message from the APSEC 2021 Chairs APSEC 2021届主席致辞

2021 28th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2021-12-01 DOI: 10.1109/apsec53868.2021.00005

引用次数: 0