2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW)最新文献

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Mining Software Engineering Team Project Work Logs to Generate Formative Assessment 挖掘软件工程团队项目工作日志以生成形成性评估

2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW) Pub Date : 2017-12-01 DOI: 10.1109/APSECW.2017.19

Khoa Le, C. Chua, X. R. Wang

{"title":"Mining Software Engineering Team Project Work Logs to Generate Formative Assessment","authors":"Khoa Le, C. Chua, X. R. Wang","doi":"10.1109/APSECW.2017.19","DOIUrl":"https://doi.org/10.1109/APSECW.2017.19","url":null,"abstract":"Supporting students in software engineering team project can be a challenge. Team leaders and supervisors may at times find it difficult to monitor students' progress and contribution. Students may either be falling behind or not contributing properly to the team. This paper describes a proposed solution that generate formative assessment feedback automatically using text data mining techniques. Various text similarity and machine learning techniques were explored and experimented to identify a suitable model for assessing student's performance and generating feedback. Utilising the students' individual weekly logs and the team's project plan, the proposed solution experimented on different text similarity techniques to match work done against work planned. The calculated similarity score and other extracted features are then applied to different machine learning algorithms, with the root mean-squared error used as the evaluation metric to identify the most suitable model. With this proposed solution, formative feedback generated can be used by team leaders and supervisors to identify team problems early on, and provide the students with necessary support. The students themselves can also reflect on their performance and address them earlier in the project phase than later.","PeriodicalId":172357,"journal":{"name":"2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125924292","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

Sentiments Analysis in GitHub Repositories: An Empirical Study GitHub存储库中的情感分析:实证研究

2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW) Pub Date : 2017-12-01 DOI: 10.1109/APSECW.2017.13

Bo Yang, Xinjie Wei, Chao Liu

引用次数: 10

Decentralization of Control Loop for Self-Adaptive Software through Reinforcement Learning 基于强化学习的自适应软件控制回路去中心化

2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW) Pub Date : 2017-12-01 DOI: 10.1109/APSECW.2017.26

Kishan Kumar Ganguly, K. Sakib

{"title":"Decentralization of Control Loop for Self-Adaptive Software through Reinforcement Learning","authors":"Kishan Kumar Ganguly, K. Sakib","doi":"10.1109/APSECW.2017.26","DOIUrl":"https://doi.org/10.1109/APSECW.2017.26","url":null,"abstract":"In a decentralized self-adaptive software, multiple control loops provide self-adaptation capabilities to the components these manage. For this, these control loops need to coordinate to continuously satisfy some local QoS goals of each managed component and global QoS goals concerning the whole system in a changing environment. This is accomplished by choosing variants of the managed system (component) that satisfy these goals. As goal conformance requires coordination, a control loop requires choosing the variant that leads to maximum goal conformance considering the variant selection strategies by other control loops. An overall goal conformance calculation mechanism is also needed that captures the local and global goal violations. This paper proposes a decentralized reinforcement learning-based self-adaptation technique considering these issues. A reinforcement learning technique, Q-learning helps to learn the maximum achievable goal conformance choosing a specific variant. The other control loop strategies are estimated by observing their variant selection and incorporated with Q-learning for better variant selection. An overall goal conformance calculation technique is also proposed that dynamically adjusts weights on the local and global goals to emphasize violated goals. The proposed approach was evaluated using a service-based Tele Assistance System. It was compared with two approaches - random variant selection and variant selection ignoring other control loop strategies. The proposed technique outperformed both with maximum overall goal conformance. The proposed dynamic weight update mechanism was compared with a static weight-based one. The dynamic technique outperformed the static one continuously satisfying all the goals.","PeriodicalId":172357,"journal":{"name":"2017 24th Asia-Pacific Software Engineering Conference Workshops (APSECW)","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132832143","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

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