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Logo Ateneo
   
Santoro Mauro

University of Milano - Bicocca

Department of Informatics, Systems and Communications (DISCo)

Viale Sarca 336 - 20126 Milano

Room: T034

Phone: (+39) 02-6448-7886

e-mail: mauro DOT santoro AT disco DOT unimib DOT it

Homepage: http://www.lta.disco.unimib.it/lta/personalPages/mauroSantoro

Current Position

Mauro is a Research Assistant (PostDoc) at the Software Testing and Analysis Laboratory (LTA) of the Department of Informatics, Systems and Communications (DISCo), University of Milano - Bicocca.

Mauro works on the EU STREP project named "PINCETTE - Validating Changes and Upgrades in Networked Software". The goal of PINCETTE is to increase the competitiveness of the European ICT industry by reducing the costs of failure detection and fault removal during software changes, thus improving the reliability of networked software systems.

Education

Ph.D. Computer Science, University of Milano - Bicocca, November 2007 - February 2011
Dissertation: Inference of Behavioral Models that Support Program Analysis
Advisors: Prof. Mauro Pezzè and Dott. Leonardo Mariani - University of Milano - Bicocca

Professional Engineer Qualification, University of Milano - Bicocca, December 2008

M.Sc Computer Science, University of Milano - Bicocca, October 2004 - July 2007
Dissertation: Studio e Sviluppo di una Tecnica di Analisi Dinamica per la Derivazione Automatica di Modelli Comportamentali di Componenti software
Advisors: Prof. Mauro Pezzè and Dott. Leonardo Mariani - University of Milano - Bicocca

B.Sc Computer Science, University of Milano - Bicocca, October 2000 - December 2004
Dissertation: Analisi Dinamica di Componenti Software
Advisors: Prof. Mauro Pezzè and Dott. Leonardo Mariani - University of Milano - Bicocca

Research Activities

Mauro’s research interests are in the field of software engineering. In particular, his main activities focus on behavioral models inference, and automatic test cases generation. Mauro is currently working on the empirical assessment of dynamic analysis techniques that infer Finite State Automata (FSA), and a on system testing technique for interactive GUI-based applications.

Inference of Behavioral Models

In the software field, behavioral models can support software engineering tasks. Behavioral models are included in all the main analysis and testing activities: models are used in program comprehension to complement the information available in specifications, are used in testing to ease test case generation, are used as oracles to verify the correctness of the executions, and are used as failure detection to automatically identify anomalous behaviors. When behavioral models are not part of specifications, automated approaches can automatically derive behavioral models from programs. The degree of completeness and soundness of the generated models depends from the kind of inferred model and the quality of the data available for the inference. When model inference techniques do not work well or the data available for the inference are poor, the many testing and analysis techniques based on these models will necessarily provide poor results.

Mauro studies the problem of inferring FSA (the model that is likely most used to describe the behavior of software systems) that describe the behavior of programs and components, and can be useful as support for testing and analysis activities. With his works, Mauro empirically investigates the limitations and the capabilities of state of the art techniques, and he defined two complimentary approaches (one black-box and one white-box) that can produce accurate behavioral models from software systems.

Testing Automation

Automatic test case generation is a key ingredient of efficient and cost-effective software verification processes. Automatically generating test cases can reduce the costs of testing and increase the effectiveness of software verification. Automatic test case generation techniques can generate many test cases that cover either functional or structural elements with high precision, reducing the human biases, and at a lower cost than manually generated and maintained test suites.

Research has focused on several aspects of automatic test case generation that span from unit to regression testing, white to black-box testing, random to model-based testing, leaving system testing poorly automated with the exceptions of capture and replay tools that replay system test cases, and GUI testing tools that check for absence of interference between GUI events. Other system testing activities, and in particular the generation of functional test cases, are largely manual and time-consuming.

Mauro defined AutoBlackTest, a technique for the automatic generation of test cases for interactive applications. AutoBlackTest interacts with an application through its GUI, learns the most relevant ways to interact with the application itself, identifies system crashes and other domain independent problems, and generates regression test suites. AutoBlackTest use reinforcement learning techniques to understand the modalities of interacting with an application through its GUI. Mauro works to extend AutoBlackTest by using ontologies that can help to produce semantically relevant interactions.

Teaching Experience

University of Milano - Bicocca, Spring 10/11,
B.Sc Course: Operating Systems (concurrency module)

University of Milano - Bicocca, Spring 10/11,
B.Sc Course: Operating Systems Lab (concurrency module)

University of Milano - Bicocca, Fall 10/11,
B.Sc Course: Java Programming Lab

University of Milano - Bicocca, Spring 09/10,
B.Sc Course: Software Engineering Lab

University of Milano - Bicocca, Spring 09/10,
B.Sc Course: Operating Systems Lab (concurrency module)

University of Milano - Bicocca, Fall 09/10,
B.Sc Course: Operating Systems Lab (UNIX module)

University of Milano - Bicocca, Spring 08/09,
B.Sc Course: Software Engineering Lab

University of Milano - Bicocca, Spring 08/09,
B.Sc Course: Software Quality Lab

University of Milano - Bicocca, Spring 08/09,
M.Sc Course: Software Testing and Analysis Lab

Publications

Ph.D. Thesis

Mauro Santoro. Inference of Behavioral Models that Support Program Analysis. Ph.D Thesis. University
of Milano - Bicocca, 2011

Book Chapters

L. Mariani and F. Pastore and M. Pezzè and M. Santoro. Mining Finite-State Automata with Annotations.
CRC Press, Mining Software Specifications: Methodologies and Applications, 2011

International Conferences and Workshops

L. Mariani and M. Pezzè and O. Riganelli and M. Santoro. AutoBlackTest: A Tool for Automatic Black-
Box Testing. ACM/IEEE proceedings of the 31st International Conference on Software Engineering
(ICSE) - Tool Demo, 2011

Leonardo Mariani and Mauro Pezzè and Oliviero Riganelli and Mauro Santoro. SEIM: Static Inference of
Interaction Models. ACM/IEEE, proceedings of the 2nd International Workshop on Principles of Engineering
Service Oriented Systems (PESOS) workshop colocated with the 32nd International Conference
of Software Engineerng (ICSE), 2010

M. Santoro. Detecting precise behavioral models. ACM, proceedings of the 7th joint meeting of the European
Software Engineering Conference (ESEC) and the ACM SIGSOFT Symposium on the Foundations
of Software Engineering (FSE) - Doctoral Symposium, 2009

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(C) Copyright 2016 - Dipartimento Informatica Sistemistica e Comunicazione - Viale Sarca, 336
20126 Milano - Edificio U14
redazioneweb@disco.unimib.it - ultimo aggiornamento di questa pagina 19/07/2011