2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)最新文献_第4页

ACSIMA: A Cyber Security Index for Mobile Health Apps ACSIMA:移动健康应用的网络安全指数

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00039

Hamza Sellak, Mohan Baruwal Chhetri, M. Grobler, Kristen Moore

{"title":"ACSIMA: A Cyber Security Index for Mobile Health Apps","authors":"Hamza Sellak, Mohan Baruwal Chhetri, M. Grobler, Kristen Moore","doi":"10.1109/ASEW52652.2021.00039","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00039","url":null,"abstract":"Mobile health (mHealth) apps can make healthcare more accessible and affordable by enabling users to perform a range of self-service activities to manage their own health and wellbeing. However, the mHealth app market is largely unregulated, potentially exposing app users to substantial cyber security risks. In this exploratory study, we present the Australian Cyber Security Index for mHealth Apps (ACSIMA) as a curated cyber security checklist that can guide the assessment of existing mHealth apps as well as the design/development of new apps. In contrast to existing mHealth app assessment frameworks, ACSIMA (1) focuses exclusively on cyber security, (2) takes a multi-stakeholder approach to cyber security assessment, and (3) is specific to the Australian digital health context. ACSIMA is aimed at raising the level of cyber security acuity among different stakeholder groups, including app users and app developers, and can be considered a first step towards the provision of reliable and trustworthy digital health services. We validate ACSIMA's usability and practicability through an online survey with the different stakeholder groups. The study finds that different stakeholder groups exhibit different levels of familiarity and importance towards the ACSIMA checklist, validating the need for raising the awareness of cyber security concerns in mHealth apps across all stakeholder groups.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128870348","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

Sustainable AI in the Cloud: Exploring Machine Learning Energy Use in the Cloud 云中的可持续人工智能:探索云中的机器学习能源使用

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00058

Paul Walsh, Jhilam Bera, V. Sharma, Vikrant S. Kaulgud, Raghotham M. Rao, Orlaith Ross

引用次数: 3

Message from the AeSIR 2021 Chairs 来自2021年AeSIR主席的信息

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/asew52652.2021.00010

引用次数: 0

An Eye Tracking Perspective on How Developers Rate Source Code Readability Rules 开发者如何评价源代码可读性规则的眼动追踪视角

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00037

Cole S. Peterson, Kang-il Park, Isaac Baysinger, Bonita Sharif

引用次数: 0

Message from the IWoR 2021 Chairs 2021年IWoR主席致辞

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/asew52652.2021.00006

引用次数: 0

Statistical Analysis of Refactoring Bug Reports in Eclipse Bugzilla Eclipse Bugzilla中重构Bug报告的统计分析

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00015

Eric Lacker, Jongwook Kim, Akash Kumar, Lipika Chandrashekar, Srilaxmi Paramaiahgari, Jasmine Howard

引用次数: 2

A First Look at Security Risks of Android TV Apps 安卓电视应用的安全隐患探析

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00023

Yonghui Liu, Li Li, Pingfan Kong, Xiaoyu Sun, Tegawendé F. Bissyandé

引用次数: 3

GPPT: A Power Prediction Tool for CUDA Applications GPPT: CUDA应用程序的功率预测工具

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00054

Gargi Alavani, Jineet Desai, S. Sarkar

引用次数: 0

Merging Datasets for Emotion Analysis 合并数据集用于情感分析

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00051

Ariadna de Arriba, M. Oriol, Xavier Franch

{"title":"Merging Datasets for Emotion Analysis","authors":"Ariadna de Arriba, M. Oriol, Xavier Franch","doi":"10.1109/ASEW52652.2021.00051","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00051","url":null,"abstract":"Context. Applying sentiment analysis is in general a laborious task. Furthermore, if we add the task of getting a good quality dataset with balanced distribution and enough samples, the job becomes more complicated. Objective. We want to find out whether merging compatible datasets improves emotion analysis based on machine learning (ML) techniques, compared to the original, individual datasets. Method. We obtained two datasets with Covid-19-related tweets written in Spanish, and then built from them two new datasets combining the original ones with different consolidation of balance. We analyzed the results according to precision, recall, F1-score and accuracy. Results. The results obtained show that merging two datasets can improve the performance of ML models, particularly the F1-score, when the merging process follows a strategy that optimizes the balance of the resulting dataset. Conclusions. Merging two datasets can improve the performance of ML models for emotion analysis, whilst saving resources for labeling training data. This might be especially useful for several software engineering activities that leverage on ML-based emotion analysis techniques.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116269088","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: 2

AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation AWARE:基于方面的情感分析数据集的应用程序审查的需求引出

2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW) Pub Date : 2021-11-01 DOI: 10.1109/ASEW52652.2021.00049

Nouf Alturaief, Hamoud Aljamaan, Malak Baslyman

{"title":"AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation","authors":"Nouf Alturaief, Hamoud Aljamaan, Malak Baslyman","doi":"10.1109/ASEW52652.2021.00049","DOIUrl":"https://doi.org/10.1109/ASEW52652.2021.00049","url":null,"abstract":"The smartphone apps market is growing rapidly which challenges apps owners to continue improving their products and to compete in the market. The analysis of users feedback is a key enabler for improvements as stakeholders can utilize it to gain a broad understanding of the successes and failures of their products as well as those of competitors. That leads to generating evidence-based requirements and enhancing the requirements elicitation activities. Aspect-Based Sentiment Analysis (ABSA) is a branch of Sentiment Analysis that identifies aspects and assigns a sentiment to each aspect. Having the aspect information adds a more accurate understanding of opinions and addresses the limited use of the overall sentiment. However, the ABSA task has not yet been investigated in the context of smartphone apps reviews and requirements elicitation. In this paper, we introduce AWARE as a benchmark dataset of 11323 apps reviews that are annotated with aspect terms, categories, and sentiment. Reviews were collected from three domains: productivity, social networking, and games. We derived the aspect categories for each domain using content analysis and validated them with domain experts in terms of importance, comprehensiveness, overlapping, and granularity level. We crowdsourced the annotations of aspect categories and sentiment polarities and performed quality control procedures. The aspect terms were annotated using a partially automated Natural Language Processing (NLP) approach and validated by annotators, which resulted in 98% correct aspect terms. Lastly, we built machine learning baselines for three tasks, namely (i) aspect term extraction using a POS tagger, (ii) aspect category classification, and (iii) aspect sentiment classification, using both Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) classifiers.","PeriodicalId":349977,"journal":{"name":"2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117205426","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