2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)最新文献_第2页

ICCLC-Committee ICCLC-Committee

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/qrs51102.2020.00012

引用次数: 0

An Analysis of Utility for API Recommendation: Do the Matched Results Have the Same Efforts? API推荐的效用分析:匹配的结果是否有相同的努力?

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00067

Huidan Li, Rensong Xie, Xianglong Kong, Lulu Wang, Bixin Li

{"title":"An Analysis of Utility for API Recommendation: Do the Matched Results Have the Same Efforts?","authors":"Huidan Li, Rensong Xie, Xianglong Kong, Lulu Wang, Bixin Li","doi":"10.1109/QRS51102.2020.00067","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00067","url":null,"abstract":"The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128269329","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

Is There A "Golden" Rule for Code Reviewer Recommendation? : —An Experimental Evaluation 代码评审推荐是否有“黄金”法则?一项实验评估

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00069

Yuanzhe Hu, Junjie Wang, Jie Hou, Shoubin Li, Qing Wang

{"title":"Is There A \"Golden\" Rule for Code Reviewer Recommendation? : —An Experimental Evaluation","authors":"Yuanzhe Hu, Junjie Wang, Jie Hou, Shoubin Li, Qing Wang","doi":"10.1109/QRS51102.2020.00069","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00069","url":null,"abstract":"Peer code review has been proven to be an effective practice for quality assurance, and widely adopted by commercial companies and open source communities as GitHub. However, identifying an appropriate code reviewer for a pull request is a non-trivial task considering the large number of candidate reviewers. Several approaches have been proposed for reviewer recommendation, yet none of them has conducted a complete comparison to explore which one is more effective. This paper aims at conducting an experimental evaluation of the commonly-used and state-of-the-art approaches for code reviewer recommendation. We begin with a systematic review of approaches for code reviewer recommendation, and choose six approaches for experimental evaluation. We then implement these approaches and conduct reviewer recommendation on 12 large-scale open source projects with 53,005 pull requests spanning two years. Results show that there is no golden rule when selecting code reviewer recommendation approaches, and the best approach varies in terms of different evaluation metrics (e.g., Top-5 Accuracy, MRR) and experimental projects. Nevertheless, TIE, which utilizes the textual similarity and file path similarity, is the most promising one. We also explore the sensitivity of these approaches to training data, and compare their time cost. This approach provides new insights and practical guidelines for choosing approaches for reviewer recommendation.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134049814","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

Which Metrics Should Researchers Use to Collect Repositories: An Empirical Study 研究人员应该使用哪些指标来收集知识库:一项实证研究

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00065

Kai Yamamoto, Masanari Kondo, Kinari Nishiura, O. Mizuno

{"title":"Which Metrics Should Researchers Use to Collect Repositories: An Empirical Study","authors":"Kai Yamamoto, Masanari Kondo, Kinari Nishiura, O. Mizuno","doi":"10.1109/QRS51102.2020.00065","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00065","url":null,"abstract":"GitHub is a huge publicly available development platform for hosting a version control system based on Git; software developers prefer to host their various software projects in GitHub. Therefore researchers who are interested in mining software repository frequently use GitHub to collect software projects as datasets. GitHub provides us with repository metrics such as popularity, contribution, and interest. We believe that such metrics are related to the quality of software; we use them to opt for studied repositories according to our research purpose. However, to the best of our knowledge, nobody has any evidence to support this assumption.Our main purpose is to provide researchers who study software quality, especially issue management, with repository metrics to select appropriate repositories for their studies. In this paper, we study the relationship between the characteristics of the issue pages of repositories that are selected by repository metrics in order to figure out the best repository metric to select proper repositories. The following findings are the highlights of our study: (1) The number of contributors that indicates the number of developers who contribute to a GitHub repository can be used to select the repositories having issue pages that are well-maintained. More specifically, such issue pages include more issues and in which developers use the labels more frequently rather than those that are selected by other metrics. (2) The number of dependencies opts for the repositories that have fewer issues and in which developers use the labels less often rather than those that are selected by other metrics.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134086852","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

Data Evaluation and Enhancement for Quality Improvement of Machine Learning 机器学习质量改进的数据评估与增强

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00014

Haihua Chen, Jiangping Chen, Junhua Ding

{"title":"Data Evaluation and Enhancement for Quality Improvement of Machine Learning","authors":"Haihua Chen, Jiangping Chen, Junhua Ding","doi":"10.1109/QRS51102.2020.00014","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00014","url":null,"abstract":"The poor quality of a dataset may produce low quality machine learning system. Therefore, transfer learning as a demonstrated effective approach for data quality improvement has been widely used for improving the quality of machine learning. However, the \"quality improvement\" brought by transfer learning in some studies was not rigorously validated or was even misleading. In this paper, we first investigate the quality problem of the datasets that were used for building a machine learning system. The system was claimed to have achieved the best performance comparing to existing work on a machine learning task. However, the \"best performance\" was due to the poor quality of the datasets as well as the incorrect validation process. Then we described an experimental study to demonstrate the effectiveness of transfer learning for improving the quality of datasets. However, the experiment results also show the quality improvement of transfer learning is not guaranteed, and a set of requirements have to be meet before applying the approach. Based on the investigation and experiment results, we propose a group of data quality criteria and evaluation approaches for quality improvement of machine learning. We investigated the research problem and explained the results through studying a machine learning system for normalizing medical concepts in social media text with open datasets.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126159426","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

Research on ensemble model of anomaly detection based on autoencoder 基于自编码器的异常检测集成模型研究

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00060

Yaning Han, Yunyun Ma, Jinbo Wang, Jianmin Wang

引用次数: 8

Convergence based Evaluation Strategies for Learning Agent of Hyper-heuristic Framework for Test Case Prioritization 基于收敛的超启发式框架学习代理测试用例优先级评估策略

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00058

Jinjin Han, Zheng Li, Junxia Guo, Ruilian Zhao

引用次数: 1

On the Defect Prediction for Large Scale Software Systems – From Defect Density to Machine Learning 大型软件系统的缺陷预测——从缺陷密度到机器学习

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00056

Satyabrata Pradhan, Venky Nanniyur, Pavan K. Vissapragada

{"title":"On the Defect Prediction for Large Scale Software Systems – From Defect Density to Machine Learning","authors":"Satyabrata Pradhan, Venky Nanniyur, Pavan K. Vissapragada","doi":"10.1109/QRS51102.2020.00056","DOIUrl":"https://doi.org/10.1109/QRS51102.2020.00056","url":null,"abstract":"As the software industry transitions to software-as-a-service (SAAS) model, there has been tremendous competitive pressure on companies to improve software quality at a much faster rate than before. The software defect prediction (SDP) plays an important role in this effort by enabling predictive quality management during the entire software development lifecycle (SDLC). The SDP has traditionally used defect density and other parametric models. However, recent advances in machine learning and artificial intelligence (ML/AI) have created a renewed interest in ML-based defect prediction among academic researchers and industry practitioners. Published studies on this subject have focused on two areas, i.e. model attributes and ML algorithms, to develop SDP models for small to medium sized software (mostly opensource). However, as we present in this paper, ML-based SDP for large scale software with hundreds of millions of lines of code (LOC) needs to address challenges in additional areas called \"Data Definition\" and \"SDP Lifecycle.\" We have proposed solutions for these challenges and used the example of a large-scale software (IOS-XE) developed by Cisco Systems to show the validity of our solutions.","PeriodicalId":301814,"journal":{"name":"2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127753842","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: 5

Dissecting Mobile Offerwall Advertisements: An Explorative Study 剖析手机付费广告:一项探索性研究

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00072

Xu Xu, Yangyu Hu, Qian Guo, Ren He, Li Li, Guoai Xu, Zhihui Han, Haoyu Wang

引用次数: 2

Quality Assurance for Machine Learning – an approach to function and system safeguarding 机器学习的质量保证——一种功能和系统保障的方法

2020 IEEE 20th International Conference on Software Quality, Reliability and Security (QRS) Pub Date : 2020-12-01 DOI: 10.1109/QRS51102.2020.00016

Alexander Poth, Burkhard Meyer, Peter Schlicht, A. Riel

引用次数: 13