Incident Ticket Analytics for IT Application Management Services

Li, Ta Hsin; Liu, Rong; Sukaviriya, Noi; Li, Ying; Yang, Jeaha; Sandin, Michael; Lee, Juhnyoung

doi:10.1109/scc.2014.80

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…Furthermore, presence of machine-generated text, e.g., error messages, stack traces or source code, might have affected our results. Similarly, to reduce the threats related to the choice of the statistical machinery we opt for the T-approach (Konietschke et al 2012) that has been successfully applied in the software engineering context (Dajsuren et al 2013;Li et al 2014;Sun et al 2015;Vasilescu et al 2013;Vasilescu et al 2013;Wang et al 2014;Yu et al 2016). …”

Section: Threats To Validitymentioning

confidence: 99%

On negative results when using sentiment analysis tools for software engineering research

Sarkar²,

et al. 2017

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Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SENTISTRENGTH and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and STACK OVERFLOW questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used.

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Section: Threats To Validitymentioning

confidence: 99%

On negative results when using sentiment analysis tools for software engineering research

Sarkar²,

et al. 2017

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“…In the work of Hsin Li et al, 1 the ticket analytics on IT application management services were focused with different key approaches using real data. The author analyses various methods on the performance of ticket analytics using the real data set.…”

Section: Related Workmentioning

confidence: 99%

Multilevel Petri net‐based ticket assignment and IT management for improved IT organization support

Mukunthan

Selvakumar

2019

Concurrency and Computation

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Summary The problem of incident management has been well studied and the ticketing has been handled by various approaches. Still, the methods suffer to produce higher growth in IT service management towards the development of IT organizations. To solve this issue, a multilevel Petri net‐based ticket assignment scheme has been proposed. The method maintains different services in different levels. According to the ticket identified, the ticket has been assigned through number of services in different levels. At each level, the status of service and ticket has been collected and maintained. Similarly, the number of ticket and service states has been generated. For each level and for each service, the method computes the popularity based on their previous ticket management support measure and the error rate. Finally, a single set of services has been selected for the assignment of ticket. At the selected composite service, the six sigma has been applied and validated for the quality of output. Based on the error rate identified, the process has been iterated until an optimal service has been identified. The proposed algorithm identifies an efficient set of services to be assigned for the ticket and improves the performance of ticket management. In addition, the proposed algorithm improves performance of organizational support greatly.

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“…İnformasiya təhlükəsizliyi insidentləri biletlərdə strukturlaşdırılmış və strukturlaşdırılmamış şəkildə saxlanılır. Hər bir biletdə isə insidentə xas olan aşağıdakı xarakteristikalar əks oluna bilər [9]:  Təsvir: hadisə sözlərlə təsvir edilir. Əlavə olaraq bir neçə biletlər üzərində çoxlu sayda metrikaları hesablamaq mümkündür.…”

Section: Bi̇let Bazasinin İnformasi̇ya Modeli̇unclassified

“…Məsələn, bir neçə bilet üzərində sıxlıq metrikasını tətbiq etmək olar. Belə ki, sıxlıq dedikdə müəyyən zaman periodu ərzində yazılan biletlərin sayı ola bilər [9]. Şəkil 1-də tipik bir insident bileti əks olunmuşdur.…”

Section: Bi̇let Bazasinin İnformasi̇ya Modeli̇unclassified