Annibale Panichella

Annibale Panichella

Assistant Professor

Delft University of Technology


I am an Assistant Professor in the Software Engineering Research Group (SERG) at Delft University of Technology (TU Delft) in Netherlands. I am an Assistant Professor in the Software Engineering Research Group (SERG) at Delft University of Technology (TU Delft) in Netherlands. I am also a research fellow in the Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, where I worked as Research Associate until January 2018.

My research interests include security testing, evolutionary testing, search-based software engineering, textual analysis, and empirical software engineering. I serve and have served as program committee member of various international conference (e.g., ICSE, GECCO, ICST and ICPC) and as reviewer for various international journals (e.g., TSE, TOSEM, TEVC, EMSE, STVR) in the fields of software engineering and evolutionary computation.


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


  • PhD in Software Engineering, 2014

    University of Salerno



Assistant Professor

Delft University of Technology

Mar 2018 – Present Delft, The Netherlands


Delft University of Technology

Jan 2015 – Sep 2016 Delft, The Netherlands


Fondazione Bruno Kessler - Security & Trust

Jan 2014 – Dec 2014 Trento, Italy


Teaching Courses

  • Context Project 2017-2018, TUDelft.
  • Software Engineering Methods 2018-2019, TUDelft.
  • Context Project 2018-2019, TUDelft.
  • Software Engineering Methods 2019-2020, TUDelft.
  • Context Project 2019-2020, TUDelft.
  • Software Testing and Reverse Engineering 2019-2020, TUDelft.


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

Automated Test Generation for Unit Testing Beyond

Recent Publications

Quickly discover relevant content by filtering publications.
(2021). Log-based Slicing for System-level Test Cases. The ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA).

(2021). EvoSuite at the SBST 2021 Tool Competition. The 14th Intl. Workshop on Search-Based Software Testing.

Code Project

(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).

(2021). Search-Based Software Re-Modularization: A Case Study at Adyen. The 43rd International Conference on Software Engineering (ICSE 2021) - Software Engineering in Practice (SEIP).

(2021). Openly teaching and structuring machine learning reproducibility. Third Workshop on Reproducible Research on Pattern Recognition.