ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal

Effective change management is crucial for businesses heavily reliant on software and services to minimise incidents induced by changes. Unfortunately, in practice it is often difficult to effectively use artificial intelligence for IT Operations (AIOps) to enhance service management, primarily due to inadequate data quality. Establishing reliable links between changes and the induced incidents is crucial for identifying patterns, improving change deployment, identifying high-risk changes, and enhancing incident response. In this research, we investigate the enhancement of traceability between changes and incidents through AIOps methods. Our approach involves an close examination of incident-inducing changes, the replication of methods linking incidents to the changes that caused them, introducing an adapted method, and demonstrating its results using historical data and practical evaluations. Our findings reveal that incident-inducing changes exhibit different characteristics dependent on context. Furthermore, a significant disparity exists between assessments based on historical data and real-world observation, with an increased occurrence of false positives when identifying links between unlabeled changes and incidents. This study highlights the complex nature of identifying links between changes and incidents, emphasising the contextual influence on AIOps method effectiveness. While we are actively working on improving the quality of current data through AIOps approaches, it remains apparent that further measures are necessary to address issues like data imbalances and promote a postmortem cultures that brings attention to the value of properly administrating tickets. A better overview of change failure rates contributes to improved risk compliance and reliable change management.

Wed 17 Apr

Displayed time zone: Lisbon change

14:00 - 15:30
Dependability and Formal methods 1Software Engineering in Practice / Demonstrations / Research Track at Maria Helena Vieira da Silva
Chair(s): Domenico Bianculli University of Luxembourg
14:00
15m
Talk
REDriver: Runtime Enforcement for Autonomous Vehicles
Research Track
Yang Sun Singapore Management University, Chris Poskitt Singapore Management University, Xiaodong Zhang , Jun Sun Singapore Management University
Pre-print
14:15
15m
Talk
Scalable Relational Analysis via Relational Bound Propagation
Research Track
Clay Stevens Iowa State University, Hamid Bagheri University of Nebraska-Lincoln
14:30
15m
Talk
Kind Controllers and Fast Heuristics for Non-Well-Separated GR(1) Specifications
Research Track
Ariel Gorenstein Tel Aviv University, Shahar Maoz Tel Aviv University, Jan Oliver Ringert Bauhaus-University Weimar
14:45
15m
Talk
On the Difficulty of Identifying Incident-Inducing Changes
Software Engineering in Practice
Eileen Kapel ING & Delft University of Technology, Luís Cruz Delft University of Technology, Diomidis Spinellis Athens University of Economics and Business & Delft University of Technology, Arie van Deursen Delft University of Technology
15:00
15m
Talk
Autonomous Monitors for Detecting Failures Early and Reporting Interpretable Alerts in Cloud Operations
Software Engineering in Practice
Adha Hrusto Lund University, Sweden, Per Runeson Lund University, Magnus C Ohlsson System Verification
15:15
7m
Talk
nvshare: Practical GPU Sharing without Memory Size Constraints
Demonstrations
Georgios Alexopoulos University of Athens, Dimitris Mitropoulos University of Athens
Pre-print
15:22
7m
Talk
Daedalux: An Extensible Platform for Variability-Aware Model Checking
Demonstrations
Sami Lazreg Visteon Electronics and Universite Cote d Azur, Maxime Cordy University of Luxembourg, Luxembourg, Simon Thrane Hansen SnT, University of Luxembourg, Axel Legay Université Catholique de Louvain, Belgium
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