2019 26th Asia-Pacific Software Engineering Conference (APSEC)最新文献_第5页

Message from the APSEC 2019 Program Chairs APSEC 2019项目主席的讲话

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/apsec48747.2019.00006

引用次数: 0

Automatic Identification of Assumptions from the Hibernate Developer Mailing List 自动识别Hibernate开发者邮件列表中的假设

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00060

Ruiyin Li, Peng Liang, Chen Yang, Georgios Digkas, A. Chatzigeorgiou, Zhuang Xiong

{"title":"Automatic Identification of Assumptions from the Hibernate Developer Mailing List","authors":"Ruiyin Li, Peng Liang, Chen Yang, Georgios Digkas, A. Chatzigeorgiou, Zhuang Xiong","doi":"10.1109/APSEC48747.2019.00060","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00060","url":null,"abstract":"During the software development life cycle, assumptions are an important type of software development knowledge that can be extracted from textual artifacts. Analyzing assumptions can help to, for example, comprehend software design and further facilitate software maintenance. Manual identification of assumptions by stakeholders is rather time-consuming, especially when analyzing a large dataset of textual artifacts. To address this problem, one promising way is to use automatic techniques for assumption identification. In this study, we conducted an experiment to evaluate the performance of existing machine learning classification algorithms for automatic assumption identification, through a dataset extracted from the Hibernate developer mailing list. The dataset is composed of 400 \"Assumption\" sentences and 400 \"Non-Assumption\" sentences. Seven classifiers using different machine learning algorithms were selected and evaluated. The experiment results show that the SVM algorithm achieved the best performance (with a precision of 0.829, a recall of 0.812, and an F1-score of 0.819). Additionally, according to the ROC curves and related AUC values, the SVM-based classifier comparatively performed better than other classifiers for the binary classification of assumptions.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125276639","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

Reinforcement Learning of Code Search Sessions 代码搜索会话的强化学习

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00068

Wei Li, Shuhan Yan, Beijun Shen, Yuting Chen

引用次数: 4

Towards a Formal Approach to Defining and Computing the Complexity of Component Based Software 基于组件的软件复杂性的形式化定义和计算方法

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00052

Yongxin Zhao, Xiujuan Zhang, Ling Shi, Gan Zeng, Feng Sheng, Shuang Liu

引用次数: 0

Towards Non-Invasive Recognition of Developers' Flow States with Computer Interaction Traces 基于计算机交互轨迹的开发人员流状态非侵入识别研究

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00048

Zhiwen Zheng, Liang Wang, Yue Cao, Yuqian Zhuang, Xianping Tao

{"title":"Towards Non-Invasive Recognition of Developers' Flow States with Computer Interaction Traces","authors":"Zhiwen Zheng, Liang Wang, Yue Cao, Yuqian Zhuang, Xianping Tao","doi":"10.1109/APSEC48747.2019.00048","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00048","url":null,"abstract":"Flow is a holistic description of people's optimal experiences during creative activities that can be characterized as being totally concentrated on, and actively involved in the task, enjoying the process of creation, and achieving a balance between one's skill and the task's challenge. Understanding software developers' flow states has attracted an increasing attention in both research and practice because of the strong link between being in flow and achieving good performance. In this paper, we study the problem of tracking and recognizing developers' flow states by tracing their computer interactions including activities of using the keyboard, mouse, IDE functions, and switching application windows. Compared to the traditional approaches that rely on self-reports or wearable sensors, a major advantage of the proposed approach is being non-invasive for not requiring any additional efforts from the developers after the training phase is completed, which is important because the developers' flow states can easily be interrupted by external interferences. Based on the captured interaction traces, we represent the developers' activities with extensive features, and propose to address the flow state recognition problem using machine learning technologies. And a hierarchical recognition model is built following the multi-dimensional construct of the flow concept, which is interpretable and effective. We develop a prototype system and conduct a 17-day field study in a medium-sized IT company in China to collect real-world data. The results show that our approach is effective by achieving the highest recognition accuracy of 92.6%, and efficient for performing real-time recognition.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131946943","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

Class Name Recommendation Based on Graph Embedding of Program Elements 基于程序元素图嵌入的类名推荐

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00073

Shintaro Kurimoto, Yasuhiro Hayase, Hiroshi Yonai, Hiroyoshi Ito, H. Kitagawa

{"title":"Class Name Recommendation Based on Graph Embedding of Program Elements","authors":"Shintaro Kurimoto, Yasuhiro Hayase, Hiroshi Yonai, Hiroyoshi Ito, H. Kitagawa","doi":"10.1109/APSEC48747.2019.00073","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00073","url":null,"abstract":"In software development, the quality of identifier names is important because it greatly affects program comprehension for developers. However, naming identifiers that appropriately represent the nature or behavior of program elements such as classes and methods is a difficult task requiring rich development experience and software domain knowledge. Although several studies proposed techniques for recommending identifier names, there are few studies targeting class names and they have limited availability. This paper proposes a novel class name recommendation approach widely available in software development. The key idea is to represent quantitatively the nature or behavior of classes by leveraging embedding technology for heterogeneous graphs. This makes it possible to recommend class names even where a previous approach cannot work. Experimental results suggest that the proposed approach can produce more accurate class name recommendation regardless of whether classes are used. In addition, a further experiment reveals a situation where the proposed approach is particularly effective.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":" 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132125381","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

Detecting Duplicate Questions in Stack Overflow via Deep Learning Approaches 利用深度学习方法检测堆栈溢出中的重复问题

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00074

Liting Wang, Li Zhang, Jing Jiang

{"title":"Detecting Duplicate Questions in Stack Overflow via Deep Learning Approaches","authors":"Liting Wang, Li Zhang, Jing Jiang","doi":"10.1109/APSEC48747.2019.00074","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00074","url":null,"abstract":"Stack Overflow is a popular question and answer website based on the software programming. Different users often ask the same questions in different ways, resulting in a large number of duplicate questions in Stack Overflow. Generally, the users with high reputation manually analyze and mark duplicate questions, which is time consuming and low efficiency. Therefore, the automatic duplicate question detection approach is demanded. We first investigate the application of deep learning models to software engineering task. Then, three deep learning models (i.e., CNN, RNN and LSTM) are applied to demonstrate whether they are effective to duplicate question detection task in Stack Overflow. In this paper, we explore three deep learning approaches DQ-CNN, DQ-RNN and DQ-LSTM based on CNN, RNN and LSTM to detect duplicate questions. The effectiveness of DQ-CNN, DQ-RNN and DQ-LSTM is evaluated by six different question groups. The experimental results show that DQ-LSTM outperforms DupPredictor, Dupe, DupePredictorRep-T and DupeRep in terms of recall-rate@5, recall-rate@10 and recall-rate@20 except for Ruby question group.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"584 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113982288","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

An Algebraic Approach to Modeling and Verifying Policy-Driven Smart Devices in IoT Systems 物联网系统中策略驱动智能设备建模和验证的代数方法

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00034

X. Chi, Min Zhang, X. Xu

引用次数: 0

Multiple Program Analysis Techniques Enable Precise Check for SEI CERT C Coding Standard 多种程序分析技术使精确检查SEI CERT C编码标准

2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/APSEC48747.2019.00019

Thu-Trang Nguyen, Toshiaki Aoki, Takashi Tomita, Iori Yamada

引用次数: 4

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2019 26th Asia-Pacific Software Engineering Conference (APSEC) Pub Date : 2019-12-01 DOI: 10.1109/apsec48747.2019.00001

引用次数: 0