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Understanding Task-Driven Information Flow in Collaborative Networks
, 2012
"... Collaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers ’ problems. In such networks, information flow among members is driven by the tasks assigned to the network, and by the expertis ..."
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Cited by 5 (1 self)
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Collaborative networks are a special type of social network formed by members who collectively achieve specific goals, such as fixing software bugs and resolving customers ’ problems. In such networks, information flow among members is driven by the tasks assigned to the network, and by the expertise of its members to complete those tasks. In this work, we analyze real-life collaborative networks to understand their common characteristics and how information is routed in these networks. Our study shows that collaborative networks exhibit significantly different properties compared with other complex networks. Collaborative networks have truncated power-law node degree distributions and other organizational constraints. Furthermore, the number of steps along which information is routed follows a truncated power-law distribution. Based on these observations, we developed a network model that can generate synthetic collaborative networks subject to certain structure constraints. Moreover, we developed a routing model that emulates task-driven information routing conducted by human beings in a collaborative network. Together, these two models can be used to study the efficiency of information routing for different types of collaborative networks – a problem that is important in practice yet difficult to solve without the method proposed in this paper.
Towards Effective Bug Triage with Software Data Reduction Techniques
"... Abstract—Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic ..."
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Abstract—Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance. Index Terms—Mining software repositories, application of data preprocessing, data management in bug repositories, bug data reduction, feature selection, instance selection, bug triage, prediction for reduction orders. 1
Recommending Resolutions for Problems Identified by Monitoring
"... Service Providers are facing an increasingly intense competitive landscape and growing industry requirements. Modern service infrastructure management focuses on the development of methodologies and tools for improving the efficiency and quality of service. It is desirable to run a service in a full ..."
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Cited by 2 (2 self)
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Service Providers are facing an increasingly intense competitive landscape and growing industry requirements. Modern service infrastructure management focuses on the development of methodologies and tools for improving the efficiency and quality of service. It is desirable to run a service in a fully automated operation environment. Automated problem resolution, however, is difficult. It is particularly difficult for the weakly-coupled service composition, since the coupling is not defined at design time. Monitoring software systems are designed to actively capture events and automatically generate incident tickets or event tickets. Repeating events generate similar event tickets, which in turn have a vast number of repeated problem resolutions likely to be found in earlier tickets. We apply a recommendation systems approach to resolution of event tickets. In addition, we extend the recommendation methodology to take into account possible falsity of some of the tickets. The paper presents an analysis of the historical event tickets from a large service provider and proposes two resolution-recommendation algorithms for event tickets utilizing historical tickets. The recommendation algorithms take into account false positives often generated by monitoring systems. An additional penalty is incorporated in the algorithms to control the number of misleading resolutions in the recommendation results. An extensive empirical evaluation on three ticket data sets demonstrates that our proposed algorithms achieve a high accuracy with a small percentage of misleading results. I.
Cold-Start Expert Finding in Community Question Answering via Graph Regularization
"... Abstract. Expert finding for question answering is a challenging problem in Community-based Question Answering (CQA) systems such as Quora. The success of expert finding is important to many real applications such as question routing and identification of best answers. Currently, many approaches of ..."
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Abstract. Expert finding for question answering is a challenging problem in Community-based Question Answering (CQA) systems such as Quora. The success of expert finding is important to many real applications such as question routing and identification of best answers. Currently, many approaches of expert findings rely heavily on the past question-answering activities of the users in order to build user models. However, the past question-answering activities of most users in real CQA systems are rather limited. We call the users who have only answered a small number of questions the cold-start users. Using the existing approaches, we find that it is difficult to address the cold-start issue in finding the experts. In this paper, we formulate a new problem of cold-start expert finding in CQA systems. We first utilize the "following relations" between the users and topical interests to build the user-to-user graph in CQA systems. Next, we propose the Graph Regularized Latent Model (GRLM) to infer the expertise of users based on both past question-answering activities and an inferred user-to-user graph. We then devise an iterative variational method for inferring the GRLM model. We evaluate our method on a well-known question-answering system called Quora. Our empirical study shows encouraging results of the proposed algorithm in comparison to the state-of-the-art expert finding algorithms.
Accelerating Problem Solving in Collaborative Social Networks
"... Problem solving is ubiquitous in organizations such as companies, institutions, and government agencies. Problems can be diverse in nature, including IT problems, expert finding, information search, decision making, etc. In large organizations, it is sometimes extremely difficult to pinpoint the rig ..."
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Problem solving is ubiquitous in organizations such as companies, institutions, and government agencies. Problems can be diverse in nature, including IT problems, expert finding, information search, decision making, etc. In large organizations, it is sometimes extremely difficult to pinpoint the right problem resolver due to the great diversity of problems and people’s expertise. Typically, when an expert is assigned with a problem, she tries to diagnose and resolve it using her own knowledge. If the problem cannot be resolved, she will then forward it, along with her diagnosis, to other experts she considers capable of resolving it. The problem may continue being routed among experts until it is resolved by the right person. The problem solving processes are social computing tasks performed in a social network, in which a node represents an
Analyzing Expert Behaviors in Collaborative Networks
"... Collaborative networks are composed of experts who coop-erate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his ..."
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Collaborative networks are composed of experts who coop-erate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns. In particular, we observe an interesting phenomenon that an expert tends to transfer a task to someone whose knowl-edge is neither too similar to nor too different from his own. Based on this observation, an expertise difference based rout-ing pattern is developed. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to com-bine them. For a held-out set of tasks, our model not only explains their real routing sequences very well, but also accu-rately predicts their completion time. Under three different quality measures, our method significantly outperforms al-l the alternatives with more than 75 % accuracy gain. In practice, with the help of our model, hypotheses on how to improve a collaborative network can be tested quickly and reliably, thereby significantly easing performance improve-ment of collaborative networks.
Expertise-Based Data Access in Content-Centric Mobile Opportunistic Networks
"... Abstract—In mobile opportunistic networks, most existing research focuses on how to choose appropriate relays to carry and forward data. Although relay selection is an important issue, other issues such as finding content from people with the right expertise are also very important since the ultimat ..."
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Abstract—In mobile opportunistic networks, most existing research focuses on how to choose appropriate relays to carry and forward data. Although relay selection is an important issue, other issues such as finding content from people with the right expertise are also very important since the ultimate goal of using mobile opportunistic network is to provide the right content to mobile users (nodes). In this paper, we study expertise-based data access in content-centric mobile opportunistic networks, where the objective is to minimize the average query delay given a sequence of queries considering node expertise, node queuing delay and communication delay. To solve this problem, we propose various query forwarding approaches under determin-istic and probabilistic expertise models. Specifically, we propose centralized approaches to assign queries based on a modified Dijkstra’s shortest path algorithm and distributed approaches in which query forwarding is based on a utility metric. Extensive simulations on both synthetic and realistic traces demonstrate that our solutions outperform existing approaches. I.
Novel Metrics for Bug Triage
"... Abstract—Bug Triaging is a vital part of issue management systems. Bug triaging deals with assigning a developer the task of an incoming bug. This activity is error prone and time consuming if done manually. There is a need for automated support to accelerate this process. The current automated bug ..."
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Abstract—Bug Triaging is a vital part of issue management systems. Bug triaging deals with assigning a developer the task of an incoming bug. This activity is error prone and time consuming if done manually. There is a need for automated support to accelerate this process. The current automated bug triaging systems exploits the text contents of the bug and the tossing relations among the developers. The automated bug triaging systems estimate the optimal bath between the first assignee of the bug and the bug resolver using the tossing relations. The metrics used for assessing the efficiency of bug triaging systems that are based on tossing relations is Mean number of Steps To Resolve (MSTR). This metric quantifies the number of steps reduced by the predicted path compared to the original path. It does not capture how far the retrieved path is in alignment with the actual path. MSTR does reveal the information regarding the extent to which the order of the developers in the retrieved path is in line with that of the original path. In addition, there are no indicators for measuring the strength of the retrieved path. In this paper, we propose two metrics (i) Path Similarity Metric which quantifies path alignment based on pair wise path alignment and (ii) Path Alignment Indicator that measures the effectiveness of the retrieved path based on degree centrality. The effectiveness of the two proposed metrics is validated using bug reports extracted from the Eclipse project.
