Edelta is a tool based on a DSL for specifying reusable libraries of metamodel refactorings. Edelta is implemented with Xtext and Xbase, thus is provides a fully-fledge Eclipse editor with syntax highlighting, code completion, error reporting and incremental building, not to mention debugging. Thanks to Xbase, it is completely interoperable with Java and its type system, allowing the developer to access any existing Java libraries. Edelta allows the developer to have an immediate view of the evolving metamodel before actually changing it, since it interprets the refactoring specifications on the fly, while the developer is typing in the editor.

Reference paper: Ludovico Iovino, Lorenzo Bettini, Alfonso Pierantonio and Davide Di Ruscio

Further details and download at

PADprof is a tool that detects software performance antipatterns from load testing and profiling data. It takes as input the profiler results provided by YourKit that show features to profile CPU, memory, and threads. It provides as output a report including, for each detected antipattern, statistics on the antipattern detection analysis and the involved suspicious methods.

Reference paper: Catia Trubiani, Alexander Bran, André van Hoorn, Alberto Avritzer, Holger Knoche.
“Exploiting Load Testing and Profiling for Performance Antipattern Detection”,
in the Journal of Information and Software Technology, Elsevier, volume 95, pp. 329-345, 2018.

PADprof can be downloaded from the following link:

SoEfTraceAnalyzer is a tool that automates the traceability between software architectural models and extra-functional analysis results by investigating the uncertainty while bridging these two domains. It makes use of patterns and antipatterns to deduce the logical consequences between the architectural elements and analysis results and automatically build a graph of traces to identify the system criticisms.

Reference paper: Catia Trubiani, Achraf Ghabi, Alexander Egyed.
“Exploiting Traceability Uncertainty between Software Architectural Models and Extra-Functional Results”,
in the Journal of Systems & Software (JSS), Elsevier, volume 125, pp. 15-34, 2017.

SoEfTraceAnalyzer can be downloaded from the following link:

PANDA (Performance Antipatterns aNd FeeDback in software Architectures) is a tool for addressing the results interpretation and the feedback generation problems by means of performance antipatterns, that are recurring solutions to common mistakes (i.e. bad practices) in the software development.

Reference paper: M. De Sanctis, C. Trubiani, V. Cortellessa, A. Di Marco, M. Flamminj.
“A Model-driven Approach to Catch Performance Antipatterns in ADL Specifications”,
in the journal of Information and Software Technology, Elsevier, volume 83, pp. 35-54, 2017.

PANDA can be downloaded from the following link:
Chaining of SR-aware and SR-unaware Service Functions

Segment Routing (SR) is a source routing paradigm that can benefit from both MPLS and IPv6 data planes to steer traffic through a set of nodes. It provides a simple and scalable way to support Service Function Chaining (SFC). In this demo, we propose an NFV architecture based on SR and implemented in Linux environment. It allows chaining of both SR-aware and SR-unaware Service Functions (SFs). In order to include SR-unaware SFs into SR SFC, we use our SR proxy implementation: srext, a Linux kernel module that handles the processing of SR information in behalf of the SR-unaware SFs. As SR-aware SFs, we use two of our implementation; SERA and SR-aware snort. SERA is a SEgment Routing Aware Firewall, which extends the Linux iptables firewall, and capable of applying the iptables rules to the inner packet of SR encapsulated traffic. SR-aware snort is an extended version of snort that can apply snort rules directly to inner packet of SR encapsulated traffic. We show the interoperability between SR-aware and SR-unaware SFs by including both of them within the same SFC.

Full description and tools available at:


Proj NameMembersTypeLink
Theoretical Foundations for MonitorabilityLuca AcetoIcelandic Research Found Project Grantdetails
DESPACE: DEtecting and Solving Performance Antipatterns in Cloud EnviromentsCatia TrubianiMicrosoft Azure Research Awarddetails

Awards and honors

Luca Aceto

Invited talks with GSSI affiliation

Catia Trubiani

  • selected for participation as Young Global Changer to The Think 20 Summit G20 Germany (acceptance: 8%), 2017.
  • outstanding Contribution in Reviewing from the Editors of the Journal of Systems and Software, Elsevier, 2016.
  • invited as panelist for the track on Women in Software Architecture, co-located with ECSA at University of Copenhagen, Denmark, 2016.
  • Best Paper Award at ECSA (conference rank A in the core classification), together with Achraf Ghabi and Alexander Egyed, 2015.
  • selected for participation to 3rd Heidelberg Laureate Forum HLF (acceptance: 10%), 2015.
  • Microsoft Azure Research Award for the project DESPACE (DEtecting and Solving Performance Antipatterns in Cloud Enviroments), 2014.

Gianlorenzo D’Angelo

  • 2016 EATCS award for the best Italian young researcher in theoretical computer science.

Roberto Verdecchia (III Year PHD Student)

  • Best Early Career Researcher Award. 15th IEEE International Conference on Software Architecture (ICSA 2018) 
  • Best Paper Award. 52th Hawaii International Conference on System Sciences (HICSS 2019)
  • ISSIP-IBM-CBA Student Paper Award for Best Industry Studies Paper. 52th Hawaii International Conference on System Sciences (HICSS 2019)
  • Runner-up Best Paper Award. 5th International Conference on ICT for Sustainability (ICT4S 2018)
  • Bronze medal – ACM Student Research Competition. 5th International Conference on Mobile Software Engineering and System(MobileSoft 2018)