2017 International Conference on Data and Software Engineering (ICoDSE)最新文献_第3页

Aspect-sentiment classification in opinion mining using the combination of rule-based and machine learning 基于规则和机器学习相结合的意见挖掘中的方面-情感分类

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285850

Zulva Fachrina, D. H. Widyantoro

引用次数: 15

Predicting defect resolution time using cosine similarity 利用余弦相似度预测缺陷解决时间

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285884

Pranjal Ambardekar, Anagha Jamthe, Mandar M. Chincholkar

{"title":"Predicting defect resolution time using cosine similarity","authors":"Pranjal Ambardekar, Anagha Jamthe, Mandar M. Chincholkar","doi":"10.1109/ICODSE.2017.8285884","DOIUrl":"https://doi.org/10.1109/ICODSE.2017.8285884","url":null,"abstract":"Defect resolution on time is one of the overriding project goals which cannot be neglected. Often projects suffer from missed deadlines due to open critical defects. This negatively impacts successful delivery of a product, resulting in loss of revenue and customer dissatisfaction. Predicting defect resolution time, though a daunting task, can alleviate this risk of missing targeted milestones. In this paper, the authors propose three supervised learning approaches leveraging cosine similarity measure, progressively improving the prediction for days to resolve (DTR) a defect. The prediction model uses historical defect data to estimate DTR for new similar defects. The first prediction approach leverages Naïve Bayes Classifier (NBC) to assess project risks by answering: Is quicker defect resolution feasible? The outcome of this analysis gives preliminary information on the resolution duration. To gain deeper insights on DTR, second approach utilizes similarity score between two defect summaries to predict DTR. To improve the prediction accuracy further, a third approach is shown, where predictions are based on statistical analysis on DTR of defects having same similarity scores. This approach yields lower error rates in predicting DTR for P2-High and P3-Medium defects, as compared to the second approach. Both the approaches however outperforms the simple approach, not involving supervised learning. These approaches can be applied over both open and closed source projects to reduce defect DTR.","PeriodicalId":366005,"journal":{"name":"2017 International Conference on Data and Software Engineering (ICoDSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133210451","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: 6

Graph clustering using dirichlet process mixture model 使用dirichlet过程混合模型的图聚类

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285862

I. Atastina, B. Sitohang, G. A. S. Putri, V. Moertini

引用次数: 2

Designing dashboard visualization for heterogeneous stakeholders (case study: ITB central library) 为异构涉众设计仪表板可视化(案例研究:ITB中央图书馆)

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285872

Tjan Marco Orlando, W. Sunindyo

{"title":"Designing dashboard visualization for heterogeneous stakeholders (case study: ITB central library)","authors":"Tjan Marco Orlando, W. Sunindyo","doi":"10.1109/ICODSE.2017.8285872","DOIUrl":"https://doi.org/10.1109/ICODSE.2017.8285872","url":null,"abstract":"One of the media that can be used to visualize data and the results of its analysis is dashboard. Currently in the dashboard development there have been many methodologies that can be used as a reference. However, the existing methodology does not specify the steps necessary to ensure that the dashboard development is able to accommodate heterogeneous stakeholders, in which each stakeholder has different needs and activities. In ITB central library, there has been adequate data storage method, in the form of database. However, ITB central library does not yet have a dashboard as a medium that can support the use of data by heterogeneous stakeholders. This research aims to develop dashboard of ITB central library for heterogeneous stakeholders. The dashboard is expected to support data utilization by stakeholders, both for analytical and administrative purposes. In this research also conducted a study related to dashboard development methodology, for further modification to show in detail dashboard development steps to accommodate heterogeneous stakeholders. Evaluation of dashboard implementation result is conducted empirically, involving sample from stakeholder of ITB central library. The evaluation uses two existing standardized usability questionnaire, System Usability Scale (SUS) and The Usability Metric for User Experience (UMUX). In the evaluation, it is also compiled comments from all evaluation participants to find out how far the dashboard can meet the needs of each stakeholder involved.","PeriodicalId":366005,"journal":{"name":"2017 International Conference on Data and Software Engineering (ICoDSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129978490","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: 7

Arduviz, a visual programming IDE for arduino Arduviz, arduino的可视化编程IDE

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285871

Adin Baskoro Pratomo, Riza Satria Perdana

引用次数: 12

Extensible analysis tool for trajectory pattern mining 用于轨迹模式挖掘的可扩展分析工具

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285859

Vanya Deasy Safrina, Saiful Akbar

{"title":"Extensible analysis tool for trajectory pattern mining","authors":"Vanya Deasy Safrina, Saiful Akbar","doi":"10.1109/ICODSE.2017.8285859","DOIUrl":"https://doi.org/10.1109/ICODSE.2017.8285859","url":null,"abstract":"The capabilities of moving object data collection have been increasing parallel with the development pace of technologies. The mobility of various moving objects can be easily generated via technologies, such as satellite and GPS. With such facilities, studies about moving object data have been increasing these past few decades, for instance, studies about trajectory pattern mining. Trajectory pattern mining is a field in moving object data mining that focuses on finding patterns from the spatial trajectory data generated from moving object data. The purposed system is an analysis tool that can run various algorithms related to trajectory pattern mining to mine trajectory of moving objects. In addition, the user interface is provided to facilitate interactive exploration and analysis of mining results. The main purpose of this tool development is to produce an extensible tool so that a new algorithm related to trajectory pattern mining can be added to the tool. This ability is considered important because the study on related topics is still growing rapidly. Extensibility of the tool is obtained by analyzing the general process from various trajectory pattern mining algorithms. The results of the analysis are then transformed into designs by utilizing the template method pattern to ensure the extensibility aspect itself. From this study, an analysis tool that implements various algorithms trajectory pattern mining is successfully built. The tool is extensible so that new algorithms from three mining categories, i.e. trajectory preprocessing, moving together pattern mining, and trajectory clustering, can be implemented into the tool by following several rules and steps while minimizing impact on existing system functions.","PeriodicalId":366005,"journal":{"name":"2017 International Conference on Data and Software Engineering (ICoDSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131411697","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

The effectiveness of using software development methods analysis by the project timeline in an Indonesian media company 以印尼某媒体公司为例，分析软件开发方法的有效性

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285890

Putri Sanggabuana Setiawan, M. I. Jambak, M. I. Jambak

引用次数: 3

Imputation of missing value using dynamic Bayesian network for multivariate time series data 多变量时间序列数据的动态贝叶斯网络缺失值估算

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285864

Steffi Pauli Susanti, F. N. Azizah

{"title":"Imputation of missing value using dynamic Bayesian network for multivariate time series data","authors":"Steffi Pauli Susanti, F. N. Azizah","doi":"10.1109/ICODSE.2017.8285864","DOIUrl":"https://doi.org/10.1109/ICODSE.2017.8285864","url":null,"abstract":"Time series and multivariate data are required to accommodate more complex decision making. Data are processed using data mining techniques in order to obtain valuable trends in the data that can be used to support in decision making processes. Unfortunately, we often encounter a lot of problems in preparing the data for data mining process. One of the problem is missing values. Missing values in data may causes inaccurate results of data processing. Imputation are used to handle missing values. In this thesis missing value are handled using Dynamic Bayesian Network (DBN). DBN is a useful technique to maintain the relationships between attributes of data. The results of the prediction are used to fill in the missing values in the data. Support Vector Regression (SVR) algorithm is used for predicting the missing values. It is chosen for its good performance in comparison to other similar algorithms. Validation of the technique is carried out by using Symmetric Mean Absolute Percentage Error (SMAPE). SMAPE used to count an error rate for prediction model. The use of the DBN of feature selection for SVR can't decrease the error rate of the model.","PeriodicalId":366005,"journal":{"name":"2017 International Conference on Data and Software Engineering (ICoDSE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121443319","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: 19

Minutia cylinder code-based fingerprint matching optimization using GPU 基于精细圆柱代码的指纹匹配GPU优化

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285880

Muhamad Visat Sutarno, A. I. Kistijantoro

引用次数: 3

Web application fuzz testing Web应用程序模糊测试

2017 International Conference on Data and Software Engineering (ICoDSE) Pub Date : 2017-11-01 DOI: 10.1109/ICODSE.2017.8285893

Ivan Andrianto, M. Liem, Y. Asnar

引用次数: 5