@article{aljedaani2022automatically,
                      title={Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study},
                      author={Aljedaani, Wajdi and Abuhaimed, Ibrahem and Rustam, Furqan and Mkaouer, Mohamed Wiem and Ouni, Ali and Jenhani, Ilyes},
                      journal={Social Network Analysis and Mining},
                      volume={12},
                      number={1},
                      pages={1--26},
                      year={2022},
                      publisher={Springer}
                    }
                    
                  

Abstract. To ensure the quality of a software system, devel- opers perform an activity known as unit testing, where they write code (known as test cases) that verifies the individual software units that make up the system. Like production code, test cases are subject to bad programming practices, known as test smells, that hurt maintenance activities. An essential part of most maintenance activities is program comprehension which involves developers reading the code to understand its behavior to fix issues or update features. In this study, we conduct a controlled experiment with 96 undergraduate computer science students to investigate the impact of two common types of test smells, namely Assertion Roulette and Eager Test, on a student's ability to debug and troubleshoot test case failures. Our findings show that students take longer to correct errors in production code when smells are present in their associated test cases, especially Assertion Roulette. We envision our findings supporting academia in better equipping students with the knowledge and resources in writing and maintaining high-quality test cases.

Research Question: To what extent do Assertion Roulette and Eager Test impact the time spent by students in debugging failing test cases?

Experimental Material

Source Code Files

Tutorial Artifacts

Tutorial Documentation