2015 24th Australasian Software Engineering Conference最新文献_第3页

Using Metamorphic Testing to Improve Dynamic Symbolic Execution 使用变形测试改进动态符号执行

2015 24th Australasian Software Engineering Conference Pub Date : 2015-09-28 DOI: 10.1109/ASWEC.2015.16

Eman Alatawi, Tim Miller, H. Søndergaard

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引用次数: 4

Automating SmartPhone Automation: Enhancing User Experience with Behaviour Learning 智能手机自动化:用行为学习增强用户体验

2015 24th Australasian Software Engineering Conference Pub Date : 2015-09-28 DOI: 10.1109/ASWEC.2015.22

Dhanish Semar, Ted Chen, Rashina Hoda

引用次数: 0

Automation of Test Case Generation from Behavior Tree Requirements Models 从行为树需求模型生成测试用例的自动化

2015 24th Australasian Software Engineering Conference Pub Date : 2015-09-28 DOI: 10.1109/ASWEC.2015.23

P. Lindsay, Sentot Kromodimoeljo, P. Strooper, Mohamed Almorsy

引用次数: 3

Design Patterns for Models of Interactive Systems 交互系统模型的设计模式

2015 24th Australasian Software Engineering Conference Pub Date : 2015-09-01 DOI: 10.1109/ASWEC.2015.30

Judy Bowen, S. Reeves

引用次数: 8

Who Will Answer My Question on Stack Overflow? 谁会回答我关于堆栈溢出的问题?

2015 24th Australasian Software Engineering Conference Pub Date : 2015-09-01 DOI: 10.1109/ASWEC.2015.28

Morakot Choetkiertikul, Daniel Avery, K. Dam, T. Tran, A. Ghose

{"title":"Who Will Answer My Question on Stack Overflow?","authors":"Morakot Choetkiertikul, Daniel Avery, K. Dam, T. Tran, A. Ghose","doi":"10.1109/ASWEC.2015.28","DOIUrl":"https://doi.org/10.1109/ASWEC.2015.28","url":null,"abstract":"Stack Overflow is a highly successful Community Question Answering (CQA) service for software developers with more than three millions users and more than ten thousand posts per day. The large volume of questions makes it difficult for users to find questions that they are interested in answering. In this paper, we propose a number of approaches to predict who will answer a new question using the characteristics of the question (i.e. Topic) and users (i.e. Reputation), and the social network of Stack Overflow users (i.e. Interested in the same topic). Specifically, our approach aims to identify a group of users (candidates) who have the potential to answer a new question by using feature-based prediction approach and social network based prediction approach. We develop predictive models to predict whether an identified candidate answers a new question. This prediction helps motivate the knowledge exchanging in the community by routing relevant questions to potential answerers. The evaluation results demonstrate the effectiveness of our predictive models, achieving 44% precision, 59% recall, and 49% F-measure (average across all test sets). In addition, our candidate identification techniques can identify the answerers who actually answer questions up to 12.8% (average across all test sets).","PeriodicalId":310799,"journal":{"name":"2015 24th Australasian Software Engineering Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127634406","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: 26