Dear Colleagues,
We invite submissions to the special issue of the Performance Evaluation Journal by Elsevier, on the theme:
*"Artificial Intelligence for Performance and Reliability Evaluation of Software Systems" * The deadline for paper submission is May 15th, 2026.
Details can be found below and at the following link: https://www.sciencedirect.com/special-issue/329639/artificial-intelligence-f...
Kind regards,
Laura Carnevali, Pengfei Chen, Evgenia Smirni
*CALL FOR PAPERS*
Nowadays software systems have become deeply pervasive, with a wide variety of applications (e.g., enterprise architectures, web services, artificial intelligence, mobile app) in several domains (e.g., IoT systems, cyber-physical systems, cloud systems, automotive driving systems). By leveraging technological advancements in hardware and communication, software systems have grown in scale, complexity, and inter-dependence, thus introducing new challenges in performance and reliability evaluation. In fact, in distributed and heterogeneous environments, performance and reliability may be affected by many different factors (e.g., software architecture, hardware infrastructure, network communication, runtime environment).
Although lots of effort has been contributed to performance and reliability evaluation of software systems, challenges still exist. Recently, Artificial Intelligence (AI) and Machine Learning (ML) methods provide powerful tools to develop descriptive, predictive, and prescriptive analytics, being able to learn the system behavior from observed data, detect anomalies at run time, and then trigger proactive remediation. At the same time, notable challenges are introduced as well, e.g., concerned with availability and quality of data, cost of re-training, and interpretability of results.
This special issue solicits unpublished works on novel solutions that leverage AI, and in particular ML, to assess and improve performance and reliability of software systems. It is intended for researchers, engineers, and practitioners who study and work on AI/ML methods for software engineering as well as those interested in performance and reliability engineering in general. Works solely focused on improving classification or regression performance of AI/ML models (e.g., in terms of metrics such as accuracy, recall, F1 score) are outside the scope of this special issue. Papers are expected to demonstrate advances to performance and/or reliability evaluation methods.
This special issue seeks submissions of full-length original research articles. Short communications and surveys are not in the scope of this special issue.
Topics of interest for this special issue include, but are not limited to, the following:
*AI/ML for performance evaluation of software systems*
- Deep learning for performance anomaly detection
- Explainable AI for performance diagnosis and prediction
- Forecasting approaches for workload characterization and prediction
- Data-driven performance profiling, benchmarking, and testing
*AI/ML for reliability evaluation of software systems*
- Neuro-symbolic approaches for reliability engineering
- LLMs for fault localization and root-cause analysis
- Generative AI for fault injection
- Clustering techniques for alert grouping and attribution
- Time-series analysis for predictive maintenance
*Applications in cutting-edge software domains*
Edge-to-cloud computing systems
Microservices architectures
Cyber-physical systems and real-time systems
Software-defined networks
LLM systems
*MANUSCRIPT SUBMISSION INFORMATION*
General information for submitting papers to PEVA can be found at Guide for Authors - Performance Evaluation <https://www.sciencedirect.com/journal/performance-evaluation/publish/guide-f...>.
Authors should submit their manuscripts to the Performance Evaluation Editorial System (EM) at Submission site for Performance Evaluation <https://www.editorialmanager.com/peva/default2.aspx>, and *select "VSI:AI for PRE of SW" when they reach the “Article Type” step in the submission process.*
*EXPECTED TIMELINE*
- Manuscript submission deadline: May 15th, 2026
- First review round completed: September 15th, 2026
- Revised manuscripts due: December 15th, 2026
- Final notification: February 15th, 2027
- Publication: June 1st, 2027