Efficient ticket routing by resolution sequence mining

Shao, Qihong; Chen, Yi; Shu, Tao; Yan, Xifeng; Anerousis, Nikos

doi:10.1145/1401890.1401964

Cited by 65 publications

(74 citation statements)

References 23 publications

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“…For example, [14] develops a Markov model by mining ticket resolution sequences of historical tickets that were transferred from one team to another before being resolved. In [10] supervised learning techniques are used to determine the likelihood that a service request corresponds to a specific problem symptom; prior performances of teams are then analyzed to identify groups that have the necessary skills to resolve these problems.…”

Section: Related Workmentioning

confidence: 99%

Who Do You Call? Problem Resolution through Social Compute Units

Sengupta

Jain

Bhattacharya

et al. 2012

Service-Oriented Computing

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Abstract. Service process orchestration using workflow technologies have led to significant improvements in generating predicable outcomes by automating tedious manual tasks but suffer from challenges related to the flexibility required in work especially when humans are involved. Recently emerging trends in enterprises to explore social computing concepts have realized value in more agile work process orchestrations but tend to be less predictable with respect to outcomes. In this paper we use IT services management, specifically, incident management for large scale systems, to investigate the interplay of workflow systems and social computing. We apply a recently introduced concept of Social Compute Units, and flexible teams sourced based on various parameters such as skills, availability, incident urgency, etc. in the context of resolution of incidents in an IT service provider organization. Results from simulationbased experiments indicate that the combination of SCUs and workflow based processes can lead to significant improvement in key service delivery outcomes, with average resolution time per incident and number of SLO violations being at times as low as 52.7% and 27.3% respectively of the corresponding values for pure workflow based incident management.

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Section: Related Workmentioning

confidence: 99%

Who Do You Call? Problem Resolution through Social Compute Units

Sengupta

Jain

Bhattacharya

et al. 2012

Service-Oriented Computing

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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, Y. Chen, S. Tao, X. Yan, and N. Anerousis [6], bug tossing is the same as ticket routing that is transferring a problem ticket among various expert groups in search of the right resolver to the ticket, which is a well-known problem in the machine learning literature. Most approaches use various statistical models to mine workflow from activity logs.…”

Section: Related Workmentioning

confidence: 99%

Survey on Bug Triage with Software Data Reduction Techniques

2015

IJSR

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Bug triage is an essential step in the process of bug fixing. Bug triage is the process of fixing bug whose main objective is to correctly allocate a developer to a new bug further handling. Many software companies spend their most of cost in dealing with these bugs. To decrease the time cost in manual work and to enhance the working of automatic bug triage, two techniques are applied namely text classification and binary classification. In literature various papers address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. By combining the instance selection and the feature selection algorithms to simultaneously reduce the data scale and enhance the accuracy of the bug reports in the bug triage. This survey focused on various data reduction technique for bug triage. As per literature, need to develop a effective model for doing data reduction on bug data set which will reduce the scale of the data as well as increase the quality of the data., by reducing the time and cost.

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Efficient ticket routing by resolution sequence mining

Cited by 65 publications

References 23 publications

Who Do You Call? Problem Resolution through Social Compute Units

Who Do You Call? Problem Resolution through Social Compute Units

Bug Triage Using Dimensionality Reduction Technique And PSO Algorithm

Survey on Bug Triage with Software Data Reduction Techniques

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