Software Testing Laboratory
Focus:
Testing of embedded software, empirical studies of software testing, test automation, model-based testing.
The Software Testing Laboratory (STL) focuses on contemporary and future challenges in testing of embedded software systems, primarily in research projects conducted in close collaboration with industrial partners.
With an emphasis on method and tool development, as well as industrial and practical real life case studies, our research focus includes (but is not limited to) test design, model-based testing, search-based software testing, decision-support for software testing, and test automation. In short, we develop, refine, and evaluate methods, theories and tools for testing of industrial software systems.
The objectives of STL are to improve the current body of knowledge in software testing, and to disseminate our results with the broader research community as well as with our industrial partners. To achieve this, we regularly publish in the main software testing venues, try to keep an open mind, and continuously engage in diverse academic and industrial collaboration
Towards a Model of Testers' Cognitive Processes: Software Testing as a Problem Solving Approach (Nov 2020) Eduard Paul Enoiu, Gerald Tukseferi , Robert Feldt IEEE International Workshop on Human and Social Aspects of Software Quality (HASQ 2020)
Poster: Performance Testing Driven by Reinforcement Learning (Oct 2020) Mahshid Helali Moghadam, Mehrdad Saadatmand, Markus Borg , Markus Bohlin, Björn Lisper IEEE 13th International Conference on Software Testing, Validation and Verification (ICST2020)
Product Line Adoption in Industry: An Experience Report from the Railway Domain (Oct 2020) Muhammad Abbas, Robbert Jongeling, Claes Lindskog , Eduard Paul Enoiu, Mehrdad Saadatmand, Daniel Sundmark 24th ACM International Systems and Software Product Line Conference (SPLC 2020)
Agent-Based Software Testing: A Definition and Systematic Mapping Study (Oct 2020) Pavithra Perumal Kumaresen , Mirgita Frasheri , Eduard Paul Enoiu IEEE International Workshop on Automated and Intelligent Software Testing (AIST 2020)
Performance Comparison of Two Deep Learning Algorithms in Detecting Similarities Between Manual Integration Test Cases (Aug 2020) Cristina Landin, Leo Hatvani, Sahar Tahvili, Hugo Haggren , Martin Längkvist , Amy Loutfi , Anne Håkansson The Fifteenth International Conference on Software Engineering Advances (ICSEA 2020)
A Novel Methodology to Classify Test Cases Using Natural Language Processing and Imbalanced Learning (Aug 2020) Sahar Tahvili, Leo Hatvani, Enislay Ramentol , Rita Pimentel , Wasif Afzal, Francisco Herrera Engineering Applications of Artificial Intelligence (EAAI)
