Proceedings of the 11th Innovations in Software Engineering Conference最新文献

Applying Formal Methods to Design of Migrator Component of Data Masking Software 应用形式化方法设计数据屏蔽软件的迁移器组件

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172885

Aniket Kulkarni

引用次数: 2

Coupling and Cohesion Metrics for Object-Oriented Software: A Systematic Mapping Study 面向对象软件的耦合和内聚度量:系统映射研究

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172878

S. Tiwari, S. Rathore

引用次数: 17

Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach 通过引入错误类型级别粒度来验证需求评审:一种机器学习方法

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172880

Maninder Singh, Vaibhav Anu, G. Walia, Anurag Goswami

{"title":"Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach","authors":"Maninder Singh, Vaibhav Anu, G. Walia, Anurag Goswami","doi":"10.1145/3172871.3172880","DOIUrl":"https://doi.org/10.1145/3172871.3172880","url":null,"abstract":"Inspections are a proven approach for improving software requirements quality. Owing to the fact that inspectors report both faults and non-faults (i.e., false-positives) in their inspection reports, a major chunk of work falls on the person who is responsible for consolidating the reports received from multiple inspectors. We aim at automation of fault-consolidation step by using supervised machine learning algorithms that can effectively isolate faults from non-faults. Three different inspection studies were conducted in controlled environments to obtain real inspection data from inspectors belonging to both industry and from academic backgrounds. Next, we devised a methodology to separate faults from non-faults by first using ten individual classifiers from five different classification families to categorize different fault-types (e.g., omission, incorrectness, and inconsistencies). Based on the individual performance of classifiers for each fault-type, we created targeted ensembles that are suitable for identification of each fault-type. Our analysis showed that our selected ensemble classifiers were able to separate faults from non-faults with very high accuracy (as high as 85-89% for some fault-types), with a notable result being that in some cases, individual classifiers performed better than ensembles. In general, our approach can significantly reduce effort required to isolate faults from false-positives during the fault consolidation step of requirements inspections. Our approach also discusses the percentage possibility of correctly classifying each fault-type.","PeriodicalId":199550,"journal":{"name":"Proceedings of the 11th Innovations in Software Engineering Conference","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132603511","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: 15

State-of-the-art Big Data Security Taxonomies 最先进的大数据安全分类

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172886

Madhan Kumar Srinivasan, P. Revathy

引用次数: 5

Change Vector Tracking in Emergent Design 紧急设计中的变更矢量跟踪

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172888

Ranjith Tharayil

引用次数: 1

Feature Selection Techniques to Counter Class Imbalance Problem for Aging Related Bug Prediction: Aging Related Bug Prediction 老化相关Bug预测中对抗类失衡问题的特征选择技术:老化相关Bug预测

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172872

L. Kumar, A. Sureka

{"title":"Feature Selection Techniques to Counter Class Imbalance Problem for Aging Related Bug Prediction: Aging Related Bug Prediction","authors":"L. Kumar, A. Sureka","doi":"10.1145/3172871.3172872","DOIUrl":"https://doi.org/10.1145/3172871.3172872","url":null,"abstract":"Aging-Related Bugs (ARBs) occur in long running systems due to error conditions caused because of accumulation of problems such as memory leakage or unreleased files and locks. Aging-Related Bugs are hard to discover during software testing and also challenging to replicate. Automatic identification and prediction of aging related fault-prone files and classes in an object oriented system can help the software quality assurance team to optimize their testing efforts. In this paper, we present a study on the application of static source code metrics and machine learning techniques to predict aging related bugs. We conduct a series of experiments on publicly available dataset from two large open-source software systems: Linux and MySQL. Class imbalance and high dimensionality are the two main technical challenges in building effective predictors for aging related bugs. We investigate the application of five different feature selection techniques (OneR, Information Gain, Gain Ratio, RELEIF and Symmetric Uncertainty) for dimensionality reduction and five different strategies (Random Under-sampling, Random Oversampling, SMOTE, SMOTEBoost and RUSBoost) to counter the effect of class imbalance in our proposed machine learning based solution approach. Experimental results reveal that the random under-sampling approach performs best followed by RUSBoost in-terms of the mean AUC metric. Statistical significance test demonstrates that there is a significant difference between the performance of the various feature selection techniques. Experimental results shows that Gain Ratio and RELEIF performs best in comparison to other strategies to address the class imbalance problem. We infer from the statistical significance test that there is no difference between the performances of the five different learning algorithms.","PeriodicalId":199550,"journal":{"name":"Proceedings of the 11th Innovations in Software Engineering Conference","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130554362","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: 17

Actor-Based Methods, Concepts and Tools for Analysing Emergent Behaviour: An Introduction to a Model Based Approach 分析突发行为的基于行动者的方法、概念和工具:基于模型的方法介绍

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3180077

T. Clark, V. Kulkarni, Souvik Barat, B. Barn

引用次数: 0

Preliminary Causal Discovery Results with Software Effort Estimation Data 基于软件工作量估算数据的初步因果发现结果

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172876

Anandi Hira, B. Boehm, R. Stoddard, M. Konrad

{"title":"Preliminary Causal Discovery Results with Software Effort Estimation Data","authors":"Anandi Hira, B. Boehm, R. Stoddard, M. Konrad","doi":"10.1145/3172871.3172876","DOIUrl":"https://doi.org/10.1145/3172871.3172876","url":null,"abstract":"Correlation does not imply causation. Though this is a well-known fact, most analyses depend on correlation as proof of relationships that are often treated as causal. Causal discovery, also referred to as causal model search, involves the application of statistical methods to identify causal relationships from conditional independences (and/or other statistical relationships) in the data. Though software cost estimation models use both domain knowledge and statistics, to date, there has yet to be a published report describing the evaluation of a software dataset using causal discovery. Two of the authors have previously used regression analysis to evaluate the effectiveness of the International Function Points User Group (IFPUG)'s and the Common Software Measurement International Consortium (COSMIC)'s functional size measurement methods for analyzing the Unified Code Count (UCC)1's dataset of maintenance tasks. Using the same dataset, the authors will report in this paper on what types of information causal discovery provides, and how they differ from correlation tests. This paper will introduce causal discovery to software engineering research, and its use in the future may impact how software effort models are built.","PeriodicalId":199550,"journal":{"name":"Proceedings of the 11th Innovations in Software Engineering Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125615513","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

Software 4.0: 'How' of building 'Next-Gen' systems 软件4.0:如何构建“下一代”系统

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172890

Abhay Pendse, Haresh Amre

引用次数: 2

Towards a Domain-Specific Language for the Renarration of Web Pages 面向Web页面重命名的领域特定语言

Proceedings of the 11th Innovations in Software Engineering Conference Pub Date : 2018-02-09 DOI: 10.1145/3172871.3172873

Gollapudi V. R. J. Sai Prasad, S. Chimalakonda, Venkatesh Choppella

引用次数: 0