Generative models for ticket resolution in expert networks

Miao, Gengxin; Moser, Louise E.; Yan, Xifeng; Shu, Tao; Chen, Yi; Anerousis, Nikos

doi:10.1145/1835804.1835897

Cited by 35 publications

(53 citation statements)

References 15 publications

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“…Table 3 lists the details of ten data sets after data preparation. [17] Fig 1. Algorithms for instance selection, feature selection, and bug triage.…”

Section: B Experimental Evaluation: Data Sets and Evaluationmentioning

confidence: 99%

A Novel Approach on Towards Effective Bug Triage and Improve the Quality of Bug Data

Praveen¹,

Reddy

Nabi

2017

IJARCSSE

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In order to diminution the time cost in physical work, text classification practices are pragmatic to conduct automatic bug triage. Presently, software companies spend over 45 percent of cost in dealing with software bugs. A foreseeable step of fixing bugs is bug triage, which ambitions to decorously consign a developer to a new bug. In this scheme, it is addressed the problem of data reduction for bug triage, to reduce the scale and progress the reputation of bug data. It is here combined with instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To define the order of applying instance selection and feature selection, it is extract attributes from historical bug data sets and build a predictive model for a new bug data set. It is practically scrutinized the enactment of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance.

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“…Table 3 lists the details of ten data sets after data preparation. [17] Fig 1. Algorithms for instance selection, feature selection, and bug triage.…”

Section: B Experimental Evaluation: Data Sets and Evaluationmentioning

confidence: 99%

A Novel Approach on Towards Effective Bug Triage and Improve the Quality of Bug Data

Praveen¹,

Reddy

Nabi

2017

IJARCSSE

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“…In data mining, the difficulties of bug triage narrate to the problems of expert finding (e.g., [6], [24]) and ticket routing (e.g., [16], [19]). In contrast to the broad areas in professional outcome or ticket routing, bug triage only focuses on allocating developers for bug reports.…”

Section: ) Bug Triagementioning

confidence: 99%

Bug Triage Using Dimensionality Reduction Technique And PSO Algorithm

Amritha¹,

Rani²

2016

IJECS

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Abstract-The process of fixing bug is bug triage that aims to properly assign a developer to a new bug. Software companies pay out most of their expenses in dealing with these bugs. To reduce time and cost of bug triaging, an automated approach is developed to predict a developer with relevant experience to solve the new coming report. In proposed approach data reduction is done on bug data set which will reduce the scale of the data as well as increase the quality of the data. Instance selection and feature selection is also used simultaneously with historical bug data. Previously, text classification techniques are applied to conduct bug triage. The problem here is to get quality bug data sets as they are of very huge in size. In the proposed system, the problems of reducing the size and to improve the quality of bug data are addressed.First, pre-processing is done to the remove unimportant attributes and to identify missing terms. Then instance selection is combined with feature selection by using Dimensionality reduction technique to simultaneously reduce data size on the bug dimension and the word dimension. By using PSO algorithm, the reduction order is determined using fitness value. It is used to produce quality bug data set. The results show that the proposed system can effectively reduce the data size and improve the accuracy of bug triage. The proposed system provides an approach to leveraging techniques on data processing to form reduced and high eminence bug data in software improvement and maintenance.

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“…Shao et al [18] propose a sequence mining algorithm to improve the efficiency of task resolution in IT service. Miao et al [12] develop generative models and recommend better routing by considering both task routing sequences and task contents. In [22], Zhang et al study the resolution of prediction tasks, which are to obtain probability assessments for a question of interest.…”

Section: Figure 1: a Sample Collaborative Networkmentioning

confidence: 99%

Analyzing expert behaviors in collaborative networks

Sun

Srivatsa

Tan

et al. 2014

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Collaborative networks are composed of experts who cooperate 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 knowledge is neither too similar to nor too different from his own. Based on this observation, an expertise difference based routing 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 combine them. For a held-out set of tasks, our model not only explains their real routing sequences very well, but also accurately predicts their completion time. Under three different quality measures, our method significantly outperforms all 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 improvement of collaborative networks. Source Code

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Generative models for ticket resolution in expert networks

Cited by 35 publications

References 15 publications

A Novel Approach on Towards Effective Bug Triage and Improve the Quality of Bug Data

A Novel Approach on Towards Effective Bug Triage and Improve the Quality of Bug Data

Bug Triage Using Dimensionality Reduction Technique And PSO Algorithm

Analyzing expert behaviors in collaborative networks

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