Annibale Panichella

Annibale Panichella

Associate Professor

Delft University of Technology

Biography

I am an Associate Professor in the Software Engineering Research Group (SERG) at Delft University of Technology (TU Delft) in the Netherlands. I am the head of the Computation Intelligence for Software Engineering Lab (CISELab) within SERG. My research interests include security testing, software testing, search-based software engineering, testing for AI, empirical software engineering. I serve and have served as a program committee member of various international conferences (e.g., ICSE, ESEC/FSE, ISSTA, GECCO, ICST) and as a reviewer for various international journals (e.g., TSE, TOSEM, TEVC, EMSE, STVR) in the fields of software engineering and evolutionary computation.

Interests

  • Search-based Software Engineering
  • Test Case Generation
  • Testing for AI
  • Security Testing
  • Regression Testing
  • Empirical Software Engineering
  • Evolutionary Computation

Education

  • PhD in Software Engineering, 2014

    University of Salerno

Experience

 
 
 
 
 

Associate Professor

Delft University of Technology

Jan 2023 – Present Delft, The Netherlands
 
 
 
 
 

Assistant Professor

Delft University of Technology

Mar 2018 – Dec 2022 Delft, The Netherlands
 
 
 
 
 

Research Fellow

Interdisciplinary Centre for Security, Reliability and Trust (SnT) - University of Luxembourg

Feb 2018 – Dec 2020 Luxembourg
 
 
 
 
 

Research Associate

Interdisciplinary Centre for Security, Reliability and Trust (SnT) - University of Luxembourg

Oct 2016 – Feb 2018 Luxembourg
 
 
 
 
 

Post-doc

Delft University of Technology

Jan 2015 – Sep 2016 Delft, The Netherlands
 
 
 
 
 

Scientific Developer

Fondazione Bruno Kessler - Security & Trust

Jan 2014 – Dec 2014 Trento, Italy

Projects

DevOps for Complex Cyber-physical Systems

Emerging Cyber-Physical Systems (CPS)—from robotics, transportation, to medical devices—play a crucial role in the quality of life of European citizens and the future of the European economy. Increasing automation to such an extent, however, gives rise to many challenges, at the crux of which lies the hardware and software symbiosis. COSMOS proposes to overcome the strain on developing and evolving high-quality, dependable CPS by employing two key technologies: DevOps and Artificial Intelligence (AI). These technologies offer the potential to address CPS development, verification, and evolution.

Software Testing Amplification for the DevOps Team

STAMP stands for Software Testing AMPlification. Leveraging advanced research in automatic test generation, STAMP aims at pushing automation in DevOps one step further through innovative methods of test amplification. STAMP will reuse existing assets (test cases, API descriptions, dependency models), in order to generate more test cases and test configurations each time the application is updated.

University Blockchain Research Initiative

UBRI is a partnership between Ripple and top universities around the world to support academic research, technical development and innovation in blockchain, cryptocurrency and , digital payments. Ripple is providing both financial and technical resources to university partners and collaborates with faculty and students on research and technical projects.

Recent & Upcoming Talks

Do Tests Generated by AI Help Developers? Open Challenges, Applications, and Opportunities

Mar 18, 2022 11:17 AM Virtual
Slides

What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection

May 27, 2021 11:17 AM Virtual

Revisiting test smells in automatically generated tests: limitations, pitfalls, and opportunities

Sep 27, 2020 11:17 AM Virtual
Video

Automated Repair of Feature Interaction Failures in Automated Driving Systems

Jul 18, 2020 11:17 AM Virtual
Slides Video

Automated Test Generation for Unit Testing Beyond

Oct 28, 2019 11:17 AM Hotel De Wageningsche Berg, Wageningen
Slides
See all talks

Featured Publications

Annibale Panichella, Giuseppe Di Domenico (2023). A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode Sorter. The Genetic and Evolutionary Computation Conference (GECCO 2023).

Preprint Code

Leonhard Applis, Ruben Marang, Annibale Panichella (2023). Searching for Quality: Genetic Algorithms and Metamorphic Testing for Software Engineering ML. The Genetic and Evolutionary Computation Conference (GECCO 2023).

Preprint

Pouria Derakhshanfar, Xavier Devroey, Annibale Panichella, Andy Zaidman, Arie van Deursen (2022). Generating Class-Level Integration Tests Using Call Site Information. IEEE Transactions on Software Engineering (TSE 2022).

Preprint Code

Annibale Panichella, Sebastiano Panichella, Gordon Fraser, Anand Ashok Sawant, Vincent Hellendoorn (2022). Test Smells 20 Years Later: Detectability, Validity, and Reliability. Empirical Software Engineering (EMSE 2022).

Preprint

Mitchell Olsthoorn, Arie van Deursen, Annibale Panichella (2022). Guiding Automated Test Case Generation for Transaction-Reverting Statements in Smart Contracts. The 38th IEEE International Conference on Software Maintenance and Evolution (ICSME 2022).

Preprint Code Project

Annibale Panichella (2022). An Improved Pareto Front Modeling Algorithm for Large-scale Many-Objective Optimization. The Genetic and Evolutionary Computation Conference (GECCO 2022).

Preprint Code Dataset

Matthías Páll Gissurarson, Leonhard Applis, Annibale Panichella, Arie van Deursen, David Sands (2022). PropR: Property-Based Automatic Program Repair. The 44th IEEE/ACM International Conference on Software Engineering (ICSE 2022).

Annibale Panichella, Cynthia C. S. Liem (2021). What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection. The 43rd IEEE/ACM International Conference on Software Engineering 2021 - New Ideas and Emerging Results (ICSE-NIER).

Mitchell Olsthoorn, Arie van Deursen, Annibale Panichella (2020). Generating Highly-structured Input Data by Combining Search-based Testing and Grammar-based Fuzzing. The 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) - New Ideas and Emerging Results (NIER) track.

Preprint Code Project

Raja Ben Abdessalem, Annibale Panichella, Shiva Nejati, Lionel Briand, Thomas Stifter (2020). Automated Repair of Feature Interaction Failures in Automated Driving Systems. The ACM SIGSOFT International Symposium on Software Testing and Analysis.

Preprint Slides Video

Annibale Panichella (2019). An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-objective Optimization. The Genetic and Evolutionary Computation Conference.

Preprint Code Dataset Slides

Annibale Panichella, Fitsum M. Kifetew, Paolo Tonella (2018). Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets. Transactions on Software Engineering.

Code Dataset

Dennis Appelt, Cu D. Nguyen, Annibale Panichella, Lionel Briand (2018). A Machine Learning- Driven Evolutionary Approach for Testing Web Application Firewalls. IEEE Transaction of Reliability.

Popular Topics

Code Smell Crash Replication Debugging Deep Learning Empirical Software Engineering Evolutionary Computation EvoSuite Fuzzing Information Retrieval Log Analysis Machine Learning Many-objective Optimization Meta-heuristics Multi-objective Optimization Mutation Testing Regression Testing Search-based Software Engineering Self-driving Cars Software Testing Test Case Generation

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