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<p>Dear Colleagues,<br>
<br>
We invite submissions to the special issue of the
Performance Evaluation Journal by Elsevier, on the theme:<br>
<br>
<b>"Artificial Intelligence for Performance and Reliability
Evaluation of Software Systems"<br>
</b> <br>
The deadline for paper submission is May 15th, 2026.<br>
<br>
Details can be found below and at the following link:<br>
<a class="moz-txt-link-freetext"
href="https://www.sciencedirect.com/special-issue/329639/artificial-intelligence-for-performance-and-reliability-evaluation-of-software-systems">https://www.sciencedirect.com/special-issue/329639/artificial-intelligence-for-performance-and-reliability-evaluation-of-software-systems</a></p>
<p>Kind regards,<br>
<br>
Laura Carnevali<br>
Pengfei Chen<br>
Evgenia Smirni<br>
<br>
---------------------</p>
<p><b>CALL FOR PAPERS</b></p>
<p>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).<br>
<br>
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.<br>
<br>
<br>
This special issue solicits unpublished works on novel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.<br>
<br>
This special issue seeks submissions of full-length original
research articles. Short communications and surveys are not
in the scope of this special issue.<br>
<br>
Topics of interest for this special issue include, but are
not limited to, the following:</p>
<p><b>AI/ML for performance evaluation of software systems</b></p>
<ul>
<li>Deep learning for performance anomaly detection</li>
<li>Explainable AI for performance diagnosis and prediction</li>
<li>Forecasting approaches for workload characterization and
prediction</li>
<li>Data-driven performance profiling, benchmarking, and
testing</li>
</ul>
<p><b>AI/ML for reliability evaluation of software systems</b>
</p>
<ul>
<li>Neuro-symbolic approaches for reliability engineering</li>
<li>LLMs for fault localization and root-cause analysis</li>
<li>Generative AI for fault injection</li>
<li>Clustering techniques for alert grouping and attribution</li>
<li>Time-series analysis for predictive maintenance</li>
</ul>
<b>Applications in cutting-edge software domains</b>
<ul>
<li>Edge-to-cloud computing systems</li>
<li>Microservices architectures</li>
<li>Cyber-physical systems and real-time systems</li>
<li>Software-defined networks</li>
<li>LLM systems</li>
</ul>
<p><b><br>
</b></p>
<p><b>MANUSCRIPT SUBMISSION INFORMATION</b><br>
</p>
<p>General information for submitting papers to PEVA can be
found at <a
href="https://www.sciencedirect.com/journal/performance-evaluation/publish/guide-for-authors">Guide
for Authors - Performance Evaluation</a>. <br>
<br>
Authors should submit their manuscripts to the Performance
Evaluation Editorial System (EM) at <a
href="https://www.editorialmanager.com/peva/default2.aspx">Submission
site for Performance Evaluation</a>, and <b>select
"VSI:AI for PRE of SW" when they reach the \u201cArticle Type\u201d
step in the submission process.</b><br>
</p>
<p><b>EXPECTED TIMELINE</b></p>
<ul>
<li>Manuscript submission deadline: May 15th, 2026</li>
<li>First review round completed: September 15th, 2026</li>
<li>Revised manuscripts due: December 15th, 2026</li>
<li>Final notification: February 15th, 2027</li>
<li>Publication: June 1st, 2027</li>
</ul>
<p><br>
</p>
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<p><br>
</p>
<p><br>
</p>
<div class="moz-signature">-- <br>
<p><a
href="https://www.unifi.it/p-doc2-2022-0-A-2c2a3a2c3a27-1.html"><font
color="blue"><strong>Laura Carnevali</strong></font></a>
<br>
<strong>Associate Professor</strong>
<br>
055 2758519</p>
<p>UNIVERSITĄ DEGLI STUDI DI FIRENZE
<br>
<strong>Dipartimento di Ingegneria dell'Informazione (DINFO)</strong></p>
<p>--</p>
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