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<p>Dear Colleagues,<br>
<br>
We would like to let you know that we have extended the
deadline for the following special issue of the
Performance Evaluation Journal (Elsevier):<br>
<br>
<b>"Artificial Intelligence for Performance and
Reliability Evaluation of Software Systems"<br>
</b> <br>
The new deadline for paper submission is <b><span
style="background-color:rgb(255,255,0)">July 1st,
2026</span><span class="gmail_default"
style="font-family:monospace"></span></b>.<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This special issue solicits unpublished works on novel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: <b><span
style="background-color:rgb(255,255,0)">July 1st,
2026 (EXTENDED)</span></b></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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