Fri 8 Dec 2023 17:50 - 18:10 at Foothill E - Session 5 Chair(s): Erik Fredericks

Test case prioritization techniques have emerged as effective strategies to optimize this process and mitigate the regression testing costs. Commonly, black-box heuristics guide optimal test ordering, leveraging information retrieval (e.g., cosine distance) to measure the test case distance and sort them accordingly. However, a challenge arises when dealing with tests of varying granularity levels, as they may employ distinct vocabularies (e.g., name identifiers). In this paper, we propose to measure the distance between test cases based on the shortest path between their identifiers within the WordNet lexical database. This additional heuristic is combined with the traditional cosine distance to prioritize test cases in a multi-objective fashion. Our preliminary study conducted with two different Java projects shows that test cases prioritized with WordNet achieve larger fault detection capability (APFDc) compared to the traditional cosine distance used in the literature.

Fri 8 Dec

Displayed time zone: Pacific Time (US & Canada) change

17:30 - 18:10
Session 5RENE / NIER at Foothill E
Chair(s): Erik Fredericks Grand Valley State University
On the Impact of Tool Evolution and Case Study Size on SBSE Experiments: A Replicated Study with EvoMaster
Amid Golmohammadi Kristiania University College, Man Zhang Kristiania University, Andrea Arcuri Kristiania University College and Oslo Metropolitan University
Multi-Objective Black-box Test Case Prioritization based on WordNet Distances
Imara van Dinten Delft University of Technology, Andy Zaidman Delft University of Technology, Annibale Panichella Delft University of Technology
DOI Pre-print