Automated Quality Assessment of Incident Tickets for Smart Service Continuity

Baresi, Luciano; Quattrocchi, Giovanni; Tamburri, Damian A.; Heuvel, Willem‐Jan van den

doi:10.1007/978-3-030-65310-1_35

Cited by 4 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, we deem that analyzing such code smells pave the way to understanding how lousy coding practices affect service infrastructure continuity. 10 In particular, the goal is to identify structural code measures that characterize complex blueprints and analyze the effectiveness of candidate metric-and unsupervised learning-based techniques in detecting those blueprints. Motivated by this goal, this study aims at addressing the following research questions in the context of TOSCA:…”

Section: Research Questionsmentioning

confidence: 99%

“…In this study, we conjecture that identifying code smells in technology‐agnostic infrastructure code (i.e., TOSCA) and related metrics opens up opportunities for building a general, automated service continuity quality model designed explicitly for configuration orchestration languages. Furthermore, we deem that analyzing such code smells pave the way to understanding how lousy coding practices affect service infrastructure continuity 10 . In particular, the goal is to identify structural code measures that characterize complex blueprints and analyze the effectiveness of candidate metric‐ and unsupervised learning‐based techniques in detecting those blueprints.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

“Through the looking‐glass …” An empirical study on blob infrastructure blueprints in the Topology and Orchestration Specification for Cloud Applications

Palma

Asseldonk

Catolino

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

Infrastructure‐as‐code (IaC) helps keep up with the demand for fast, reliable, high‐quality services by provisioning and managing infrastructures through configuration files. Those files ensure efficient and repeatable routines for system provisioning, but they might be affected by code smells that negatively affect quality and code maintenance. Research has broadly studied code smells for traditional source code development; however, none explored them in the “Topology and Orchestration Specification for Cloud Applications” (TOSCA), the technology‐agnostic OASIS standard for IaC. In this paper, we investigate a prominent traditional implementation code smell potentially applicable to TOSCA: Large Class, or “Blob Blueprint” in IaC terms. We compare metrics‐based and unsupervised learning‐based detectors on a large dataset of manually validated observations related to Blob Blueprints. We provide insights on code metrics that corroborate previous findings and empirically show that metrics‐based detectors perform highly in detecting Blob Blueprints. We deem our results put forward a new research path toward dealing with this problem, for example, in the scope of fully automated service pipelines.

show abstract

Section: Research Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“Through the looking‐glass …” An empirical study on blob infrastructure blueprints in the Topology and Orchestration Specification for Cloud Applications

Palma

Asseldonk

Catolino

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…Asres, Mengistu [17] x x Nayak, Rai [15] x x Yang [18] x Amin, Lancaster [19] (x) Baresi, Quattrocchi [20] x x…”

Section: Sent Pred Othermentioning

confidence: 99%

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.

show abstract

Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-Based Textual Data Quality Enrichment

Reinhard

Dickhaut

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Automated Quality Assessment of Incident Tickets for Smart Service Continuity

Cited by 4 publications

References 15 publications

“Through the looking‐glass …” An empirical study on blob infrastructure blueprints in the Topology and Orchestration Specification for Cloud Applications

“Through the looking‐glass …” An empirical study on blob infrastructure blueprints in the Topology and Orchestration Specification for Cloud Applications

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-Based Textual Data Quality Enrichment

Contact Info

Product

Resources

About