The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform
Hongyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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iDice: Problem Identification for Emerging Issues
2016 IEEE ACM 38th International Conference on Software Engineering (ICSE), 2016Co-Authors: Hongyu Zhang, Dongmei ZhangAbstract:One challenge for maintaining a large-scale software system, especially an online service system, is to quickly respond to customer issues. The issue reports typically have many categorical attributes that reflect the characteristics of the issues. For a commercial system, most of the time the volume of reported issues is relatively constant. Sometimes, there are emerging issues that lead to significant volume increase. It is important for support engineers to efficiently and effectively identify and resolve such emerging issues, since they have impacted a large number of customers. Currently, Problem Identification for an emerging issue is a tedious and error-prone process, because it requires support engineers to manually identify a particular attribute combination that characterizes the emerging issue among a large number of attribute combinations. We call such an attribute combination effective combination, which is important for issue isolation and diagnosis. In this paper, we propose iDice, an approach that can identify the effective combination for an emerging issue with high quality and performance. We evaluate the effectiveness and efficiency of iDice through experiments. We have also successfully applied iDice to several Microsoft online service systems in production. The results confirm that iDice can help identify emerging issues and reduce maintenance effort.
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Log Clustering Based Problem Identification for Online Service Systems
2016 IEEE ACM 38th International Conference on Software Engineering Companion (ICSE-C), 2016Co-Authors: Hongyu Zhang, Yu Zhang, Xuewei ChenAbstract:Logs play an important role in the maintenance of large-scale online service systems. When an online service fails, engineers need to examine recorded logs to gain insights into the failure and identify the potential Problems. Traditionally, engineers perform simple keyword search (such as “error” and “exception”) of logs that may be associated with the failures. Such an approach is often time consuming and error prone. Through our collaboration with Microsoft service product teams, we propose LogCluster, an approach that clusters the logs to ease log-based Problem Identification. LogCluster also utilizes a knowledge base to check if the log sequences occurred before. Engineers only need to examine a small number of previously unseen, representative log sequences extracted from the clusters to identify a Problem, thus significantly reducing the number of logs that should be examined, meanwhile improving the Identification accuracy. Through experiments on two Hadoop-based applications and two large-scale Microsoft online service systems, we show that our approach is effective and outperforms the state-of-the-art work proposed by Shang et al. in ICSE 2013. We have successfully applied LogCluster to the maintenance of many actual Microsoft online service systems. In this paper, we also share our success stories and lessons learned.
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ICSE - iDice: Problem Identification for emerging issues
Proceedings of the 38th International Conference on Software Engineering - ICSE '16, 2016Co-Authors: Hongyu Zhang, Dongmei ZhangAbstract:One challenge for maintaining a large-scale software system, especially an online service system, is to quickly respond to customer issues. The issue reports typically have many categorical attributes that reflect the characteristics of the issues. For a commercial system, most of the time the volume of reported issues is relatively constant. Sometimes, there are emerging issues that lead to significant volume increase. It is important for support engineers to efficiently and effectively identify and resolve such emerging issues, since they have impacted a large number of customers. Currently, Problem Identification for an emerging issue is a tedious and error-prone process, because it requires support engineers to manually identify a particular attribute combination that characterizes the emerging issue among a large number of attribute combinations. We call such an attribute combination effective combination, which is important for issue isolation and diagnosis. In this paper, we propose iDice, an approach that can identify the effective combination for an emerging issue with high quality and performance. We evaluate the effectiveness and efficiency of iDice through experiments. We have also successfully applied iDice to several Microsoft online service systems in production. The results confirm that iDice can help identify emerging issues and reduce maintenance effort.
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ICSE (Companion Volume) - Log clustering based Problem Identification for online service systems
Proceedings of the 38th International Conference on Software Engineering Companion - ICSE '16, 2016Co-Authors: Hongyu Zhang, Yu Zhang, Xuewei ChenAbstract:Logs play an important role in the maintenance of large-scale online service systems. When an online service fails, engineers need to examine recorded logs to gain insights into the failure and identify the potential Problems. Traditionally, engineers perform simple keyword search (such as "error" and "exception") of logs that may be associated with the failures. Such an approach is often time consuming and error prone. Through our collaboration with Microsoft service product teams, we propose LogCluster, an approach that clusters the logs to ease log-based Problem Identification. LogCluster also utilizes a knowledge base to check if the log sequences occurred before. Engineers only need to examine a small number of previously unseen, representative log sequences extracted from the clusters to identify a Problem, thus significantly reducing the number of logs that should be examined, meanwhile improving the Identification accuracy. Through experiments on two Hadoop-based applications and two large-scale Microsoft online service systems, we show that our approach is effective and outperforms the state-of-the-art work proposed by Shang et al. in ICSE 2013. We have successfully applied LogCluster to the maintenance of many actual Microsoft online service systems. In this paper, we also share our success stories and lessons learned.
Ester Baauw - One of the best experts on this subject based on the ideXlab platform.
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development and evaluation of the Problem Identification picture cards method
Cognition Technology & Work, 2008Co-Authors: Wolmet Barendregt, M M Bekker, Ester BaauwAbstract:In this paper the development and assessment of a new formative evaluation method called the Problem Identification picture cards (PIPC) method is described. This method enables young children to express both usability and fun Problems while playing a computer game. The method combines the traditional thinking-aloud method with picture cards that children can place in a box to indicate that there is a certain type of Problem. An experiment to assess this method shows that children may express more Problems (verbally, or with a picture card, or with a combination of a picture card and a verbalisation) with the PIPC method than without this method (in which they can only indicate Problems verbally). Children in the experiment did not just replace verbalisations by using the provided picture cards and some children preferred to use the PIPC method during the test instead of the standard thinking-aloud method. The PIPC method or some aspects of the method could be a good instrument to increase the amount of information expressed by young children during an evaluation.
Baauwester - One of the best experts on this subject based on the ideXlab platform.
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Development and evaluation of the Problem Identification picture cards method
Cognition Technology & Work, 2008Co-Authors: Barendregtwolmet, M Bekkermathilde, BaauwesterAbstract:In this paper the development and assessment of a new formative evaluation method called the Problem Identification picture cards (PIPC) method is described. This method enables young children to e...
J. Johansson - One of the best experts on this subject based on the ideXlab platform.
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Problem-Identification workshop as a future-oriented macroergonomic tool for managing the work environment
2010 IEEE International Conference on Industrial Engineering and Engineering Management, 2010Co-Authors: M. A. Sanda, Y. Fältholm, L. Abrahamsson, J. JohanssonAbstract:This paper looks at the challenges that most organizations face in the management of their work environments, with respect to the tools that they can use to effectively capture both the explicit and tacit knowledge held by their employees for subsequent reuse when decisions need to be made. The Problem-Identification workshop, which is a macroergonomic tool for enhancing the management of work environment in an organization, was tested in an organization. Participants identified organizational Problems, proposed solutions to them, realistically assessed the desirability and possibility of these solutions, and finally recommended action plans to the organization for its short-term, intermediate and long-term design and management of effective work environment towards enhancing work life and productivity in the organization. It was concluded that Problem-Identification workshop is a good socio-pedagogic method that can be used as an intelligent participatory intervention tool by managers in organizations in the management of their work environments.
Wenbin Wang - One of the best experts on this subject based on the ideXlab platform.
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Problem Identification in maintenance modelling a case study
International Journal of Production Research, 2008Co-Authors: A Akbarov, A H Christer, Wenbin WangAbstract:This paper is concerned with a Problem Identification and Problem focus process in maintenance modelling. It endeavours to describe the process of moving from vague Problem understanding towards more specific Problem formulation and Problem focus in the pursuit of practical decision making. This process was conducted using several analytical tools that complemented each other such as regression analyses, snapshot modelling and delay time modelling. As in many case studies related to maintenance modelling, this study also makes use of the experience of experts. It can be seen from the paper that subjective data estimates can prove to be a useful input for modelling. The analysis shows how simple modelling of maintenance Problems can provide useful insights and better understanding of the Problem in hand.
